learning analytics: what are we optimizing for?
DESCRIPTION
edfuture.net MOOC on Current/Future State of HigherEd http://edfuture.mooc.ca/archive/12/10_29_newsletter.htmTRANSCRIPT
httptwittercomsbskmi
Learning Analytics what are we optimizing for
Simon Buckingham Shum Knowledge Media Institute The Open University UK simonbuckinghamshumnet
edfuturenet MOOC on CurrentFuture State of HigherEd
1
Knowledge Media Institute
edX ldquothis is big data giving us the chance to ask big questions about learningrdquo
2
Will the tomorrowrsquos educational researcher be
as helpless without an analytics infrastructure as
a geneticist without genome databases or a physicist without CERN
the planhellip
joined-up multi-layer analytics an analytics ecosystem
are analytics (r)evolutionary
3
the convergence of analytics layers
4
Micro individual user actions
(and hence cohort)
MacroMesoMicro Learning Analytics
Meso institution-wide
Macro regionstatenationalinternational
Micro individual user actions
(and hence cohort)
MacroMesoMicro Learning Analytics
Meso institution-wide
Macro regionstatenationalinternational
Will institutions be dazzled by the dashboards or know what
questions to ask at each level
For examples of each level of analytichellip
7 Buckingham Shum S 2012 Our Learning Analytics are Our Pedagogy Keynote Address Expanding Horizons 2012 Conference Macquarie University Sydney httpwwwslidesharenetsbsour-learning-analytics-are-our-pedagogy
The VLEmdashBImdashITS convergence
8
Micro individual user actions
(and hence cohort)
As data migrates up it enriches higher layers normally accustomed to sparse data
Meso institution-wide
Macro regionstatenationalinternational
Aggregation of user traces enriches meso + macro analytics with finer-grained process data
Micro individual user actions
(and hence cohort)
hellipwhich in turn could enrich lower layers mdash local patterns can be cross-validated
Meso institution-wide
Macro regionstatenationalinternational
Aggregation of user traces enriches meso + macro analytics with finer-grained process data
Breadth + depth from macro + meso levels could add power to micro-analytics
anatomy of an analytics ecosystem
11
A learning analytics ecosystem
12
learners
educators
A learning analytics ecosystem
13
learners
educators
A learning analytics ecosystem
14
learners
educators
A learning analytics ecosystem
15
learners
educators
data curators translators
dashboard design team
data capture design team
Where did the data come from
16
learners
Where did the data come from
17
learners
researchers educators instructional designers
theories pedagogies
assessments tools
Where did the data come from
18
learners
researchers educators instructional designers
theories pedagogies
assessments tools
technologists
Data Intent
The map is not the territory Analytics are not the end but a means The goal is to optimize the whole system
19
learners
researchers educators instructional designers
theories pedagogies
assessments tools
desi
gn feedback
intent
outcome
Optimize the system for what
20
Same outcomes but higher scores
Learning Analytics as
Evolutionary Technology
bull more engaging bull better assessed bull better outcomes
bull deliverable at scale
21
New outcomes we couldnrsquot assess before
Learning Analytics as
Revolutionary Technology
bull learner behaviours quantifiable bull interpersonal networks quantifiable
bull discourse quantifiable bull moods and dispositions quantifiable
22
ldquoWe are preparing students for jobs that do not exist yet that will use technologies that have not been invented yet in order to solve problems that are not even problems yetrdquo
ldquoShift Happensrdquo httpshifthappenswikispacescom
23
Learning analytics for this
Learning analytics for this
ldquoWhile employers continue to demand high academic standards they also now want more They want people who can adapt see connections innovate communicate and work with others This is true in many areas of work The new knowledge-based economies in particular will increasingly depend on these abilities Many businesses are paying for courses to promote creative abilities to teach the skills and attitudes that are now essential for economic successhelliprdquo
All our Futures Creativity culture amp education May 1999 24
Learning analytics for this
25
Creativity Culture and Education (2009) Changing Young Lives 2012 Newcastle CCE httpwwwcreativitycultureeducationorgchanging-young-lives-2012
Think about the analytics products and initiatives reviewed above ndash where would you locate them on these dimensions
Expert-led enquiry
Student-led enquiry
Expert-led teaching
Student-led revision
Kno
wle
dge
co
-gen
erat
ion
an
d us
e
Pre-
scrib
ed
Kno
wle
dge
Teacher agency Student agency
Repetition Abstraction Acquisition
Authenticity Agency Identity
Teaching as learning design
The Knowledge-Agency Window
Learning analytics for this
Ruth Deakin Crick Univ Bristol Centre for Systems Learning amp Leadership ldquoPedagogy of Hoperdquo httplearningemergencenet20120921pedagogy-of-hope
analytics grounded in the principles of good
assessment for learning
(not summative assessment for
grading pupils teachers institutions or nations)
27
Assessment for Learning
28
httpassessment-reform-grouporg Few learning analytics are
currently able to take o board the richness of this
original conception of assessment for learning
Assessment for Learning
29
httpassessment-reform-grouporg
Assessment for Learning
30
httpassessment-reform-grouporg
To what extent could automated
feedback be designed and evaluated with
emotional impact in mind
Assessment for Learning
31
httpassessment-reform-grouporg
Can analytics identify proxies
for such advanced qualities
Assessment for Learning
32
httpassessment-reform-grouporg
How do we provide analytics feedback
that does not disempower and de-motivate struggling
learners
analytics for learning conversations
33
Socio-cultural discourse analysis (Mercer et al OU)
bull Disputational talk characterised by disagreement and individualised decision making
bull Cumulative talk in which speakers build positively but uncritically on what the others have said
bull Exploratory talk in which partners engage critically but constructively with each others ideas
34 Mercer N (2004) Sociocultural discourse analysis analysing classroom talk as a social mode of thinking Journal of Applied Linguistics 1(2) 137-168
bull Exploratory talk in which partners engage critically but constructively with each others ideas
bull Statements and suggestions are offered for joint consideration
bull These may be challenged and counter-challenged but challenges are justified and alternative hypotheses are offered
bull Partners all actively participate and opinions are sought and considered before decisions are jointly made
bull Compared with the other two types in Exploratory talk knowledge is made more publicly accountable and reasoning is more visible in the talk
35
Socio-cultural discourse analysis (Mercer et al OU)
Mercer N (2004) Sociocultural discourse analysis analysing classroom talk as a social mode of thinking Journal of Applied Linguistics 1(2) 137-168
Analytics for identifying Exploratory talk
36
Elluminate sessions can be very long ndash lasting for hours or even covering days of a conference
It would be useful if we could identify where quality learning conversations seem to be taking place so we can recommend those sessions and not have to sit through online chat about virtual biscuits
Ferguson R and Buckingham Shum S Learning analytics to identify exploratory dialogue within synchronous text chat 1st International Conference on Learning Analytics amp Knowledge (Banff Canada 27 Mar-1 Apr 2011)
Defining indicators of Exploratory Talk
37
Category Indicator Challenge But if have to respond my view Critique However Irsquom not sure maybe Discussion of resources
Have you read more links
Evaluation Good example good point Explanation Means that our goals Explicit reasoning Next step relates to thatrsquos why Justification I mean we learned we observed Reflections of perspectives of others
Agree here is another makes the point take your point your view
Extract classified as Exploratory Talk
38
Time Contribution 242 PM I hate talking -P My question was whether gadgets were just
basically widgets and we could embed them in various web sites like Netvibes Google Desktop etc
242 PM Thanks thats great I am sure I understood everything but looks inspiring
243 PM Yes why OU tools not generic tools
243 PM Issues of interoperability
243 PM The new SocialLearn site looks a lot like a corkboard where you can add various widgets similar to those existing web start pages
243 PM What if we end up with as many appsgadgets as we have social networks and then we need a recommender for the apps
243 PM My question was on the definition of the crowd in the wisdom of crowds we acsess in the service model
243 PM there are various different flavours of widget eg Google gadgets W3C widgets etc SocialLearn has gone for Google gadgets
-60
-40
-20
0
20
40
60
80
92
8 9
32
93
6
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0
94
1
94
6
95
0
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3
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6
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00
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10
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31
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32
11
35
11
36
11
38
11
39
11
41
11
44
11
46
11
48
11
52
11
54
12
00
12
03
12
04
12
05
Average Exploratory
Discourse analytics on webinar textchat
Sheffield UK not as sunny as yesterday - still warm Greetings from Hong Kong Morning from Wiltshire sunny here
See you bye for now bye and thank you Bye all for now
Extensions to Ferguson R and Buckingham Shum S (2011) Learning Analytics to Identify Exploratory Dialogue within Synchronous Text Chat Proc 1st Int Conf Learning Analytics amp Knowledge Feb 27-Mar 1 2011 Banff ACM Press Eprint httporoopenacuk28955
Given a 25 hour webinar where in the live textchat were the most effective learning conversations Not at the start and end of a webinar but if we zoom in on a peakhellip
Discourse analytics on webinar textchat
-100
-50
0
50
100
92
8 9
40
9
50
1
000
1
007
1
017
1
031
1
045
1
104
1
117
1
126
1
132
1
138
1
144
1
152
1
203
Averag
Wei amp He extensions to Ferguson R and Buckingham Shum S (2011) Learning Analytics to Identify Exploratory Dialogue within Synchronous Text Chat Proc 1st Int Conf Learning Analytics amp Knowledge Feb 27-Mar 1 2011 Banff ACM Press Eprint httporoopenacuk28955
Classified as ldquoexploratory
talkrdquo
(more substantive for learning)
ldquonon-exploratoryrdquo
Given a 25 hour webinar where in the live textchat were the most effective learning conversations
De Liddo A Buckingham Shum S Quinto I Bachler M and Cannavacciuolo L Discourse-centric learning analytics 1st International Conference on Learning Analytics amp Knowledge (Banff 27 Mar-1 Apr 2011) httporoopenacuk25829
KMirsquos Cohere a web deliberation platform enabling semantic social network and discourse network analytics
Rebecca is playing the role of broker
connecting 2 peersrsquo contributions in meaningful ways
analytics for scholarly writing
42
Discourse analysis (Xerox Incremental Parser)
BACKGROUND KNOWLEDGE
Recent studies indicate hellip
hellip the previously proposed hellip
hellip is universally accepted
NOVELTY
new insights provide direct evidence
we suggest a new approach
results define a novel role
OPEN QUESTION hellip little is known hellip hellip role hellip has been elusive
Current data is insufficient hellip
GENERALIZING emerging as a promising approach Our understanding has grown exponentially growing recognition of the
importance
CONRASTING IDEAS hellip unorthodox view resolves hellip paradoxes hellip
In contrast with previous hypotheses
inconsistent with past findings
SIGNIFICANCE studies have provided important advances
Knowledge is crucial for understanding
valuable information from studies
SURPRISE We have recently observed surprisingly
We have identified unusual The recent discovery suggests intriguing roles
SUMMARIZING The goal of this study Here we show
Altogether our results indicate
Detection of salient sentences in scholarly reports based on the rhetorical signals authors use
Aacutegnes Saacutendor amp OLnet Project httpolnetorgnode512
De Liddo A Saacutendor Aacute and Buckingham Shum S Contested Collective Intelligence Rationale Technologies and a Human-Machine Annotation Study Computer Supported Cooperative Work 21 4-5 (2012) 417-448 httporoopenacuk31052
Human and machine analysis of a text for key contributions
19 sentences annotated 22 sentences annotated 11 sentences same as human annotation
71 sentences annotated 59 sentences annotated 42 sentences same as human annotation
Document 1
Document 2
httptechnologieskmiopenacukcohere20120109cohere-plus-automated-rhetorical-annotation De Liddo A Saacutendor Aacute and Buckingham Shum S Contested Collective Intelligence Rationale Technologies and a Human-Machine Annotation Study Computer Supported Cooperative Work 21 4-5 (2012) 417-448 httporoopenacuk31052
analytics for intepersonal networking
45
Semantic Social Network Analytics
De Liddo A Buckingham Shum S Quinto I Bachler M and Cannavacciuolo L Discourse-centric learning analytics 1st International Conference on Learning Analytics amp Knowledge (Banff 27 Mar-1 Apr 2011) httporoopenacuk25829
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Closing thoughts
54
55
ldquoThe basic question is not what can we measure
The basic question is
what does a good education look likerdquo
(Gardner Campbell)
httpchroniclecomblogstechtherapy20120502episode-95-learning-analytics-could-lead-to-wal-martification-of-college httplak12wikispacescomRecordings
56
Our analytics promote values pedagogy and assessment regimes
Are we clear which master
our analytics serve Are we happy to be judged by them
57
Will learning analytics merely turbocharge the current educational paradigm
mdash which is so often declared
not fit for purposehellip
58
hellipor will learning analytics reflect what we now know about designing authentic
engaged learning developing the new qualities that a
complex society demands
edX ldquothis is big data giving us the chance to ask big questions about learningrdquo
2
Will the tomorrowrsquos educational researcher be
as helpless without an analytics infrastructure as
a geneticist without genome databases or a physicist without CERN
the planhellip
joined-up multi-layer analytics an analytics ecosystem
are analytics (r)evolutionary
3
the convergence of analytics layers
4
Micro individual user actions
(and hence cohort)
MacroMesoMicro Learning Analytics
Meso institution-wide
Macro regionstatenationalinternational
Micro individual user actions
(and hence cohort)
MacroMesoMicro Learning Analytics
Meso institution-wide
Macro regionstatenationalinternational
Will institutions be dazzled by the dashboards or know what
questions to ask at each level
For examples of each level of analytichellip
7 Buckingham Shum S 2012 Our Learning Analytics are Our Pedagogy Keynote Address Expanding Horizons 2012 Conference Macquarie University Sydney httpwwwslidesharenetsbsour-learning-analytics-are-our-pedagogy
The VLEmdashBImdashITS convergence
8
Micro individual user actions
(and hence cohort)
As data migrates up it enriches higher layers normally accustomed to sparse data
Meso institution-wide
Macro regionstatenationalinternational
Aggregation of user traces enriches meso + macro analytics with finer-grained process data
Micro individual user actions
(and hence cohort)
hellipwhich in turn could enrich lower layers mdash local patterns can be cross-validated
Meso institution-wide
Macro regionstatenationalinternational
Aggregation of user traces enriches meso + macro analytics with finer-grained process data
Breadth + depth from macro + meso levels could add power to micro-analytics
anatomy of an analytics ecosystem
11
A learning analytics ecosystem
12
learners
educators
A learning analytics ecosystem
13
learners
educators
A learning analytics ecosystem
14
learners
educators
A learning analytics ecosystem
15
learners
educators
data curators translators
dashboard design team
data capture design team
Where did the data come from
16
learners
Where did the data come from
17
learners
researchers educators instructional designers
theories pedagogies
assessments tools
Where did the data come from
18
learners
researchers educators instructional designers
theories pedagogies
assessments tools
technologists
Data Intent
The map is not the territory Analytics are not the end but a means The goal is to optimize the whole system
19
learners
researchers educators instructional designers
theories pedagogies
assessments tools
desi
gn feedback
intent
outcome
Optimize the system for what
20
Same outcomes but higher scores
Learning Analytics as
Evolutionary Technology
bull more engaging bull better assessed bull better outcomes
bull deliverable at scale
21
New outcomes we couldnrsquot assess before
Learning Analytics as
Revolutionary Technology
bull learner behaviours quantifiable bull interpersonal networks quantifiable
bull discourse quantifiable bull moods and dispositions quantifiable
22
ldquoWe are preparing students for jobs that do not exist yet that will use technologies that have not been invented yet in order to solve problems that are not even problems yetrdquo
ldquoShift Happensrdquo httpshifthappenswikispacescom
23
Learning analytics for this
Learning analytics for this
ldquoWhile employers continue to demand high academic standards they also now want more They want people who can adapt see connections innovate communicate and work with others This is true in many areas of work The new knowledge-based economies in particular will increasingly depend on these abilities Many businesses are paying for courses to promote creative abilities to teach the skills and attitudes that are now essential for economic successhelliprdquo
All our Futures Creativity culture amp education May 1999 24
Learning analytics for this
25
Creativity Culture and Education (2009) Changing Young Lives 2012 Newcastle CCE httpwwwcreativitycultureeducationorgchanging-young-lives-2012
Think about the analytics products and initiatives reviewed above ndash where would you locate them on these dimensions
Expert-led enquiry
Student-led enquiry
Expert-led teaching
Student-led revision
Kno
wle
dge
co
-gen
erat
ion
an
d us
e
Pre-
scrib
ed
Kno
wle
dge
Teacher agency Student agency
Repetition Abstraction Acquisition
Authenticity Agency Identity
Teaching as learning design
The Knowledge-Agency Window
Learning analytics for this
Ruth Deakin Crick Univ Bristol Centre for Systems Learning amp Leadership ldquoPedagogy of Hoperdquo httplearningemergencenet20120921pedagogy-of-hope
analytics grounded in the principles of good
assessment for learning
(not summative assessment for
grading pupils teachers institutions or nations)
27
Assessment for Learning
28
httpassessment-reform-grouporg Few learning analytics are
currently able to take o board the richness of this
original conception of assessment for learning
Assessment for Learning
29
httpassessment-reform-grouporg
Assessment for Learning
30
httpassessment-reform-grouporg
To what extent could automated
feedback be designed and evaluated with
emotional impact in mind
Assessment for Learning
31
httpassessment-reform-grouporg
Can analytics identify proxies
for such advanced qualities
Assessment for Learning
32
httpassessment-reform-grouporg
How do we provide analytics feedback
that does not disempower and de-motivate struggling
learners
analytics for learning conversations
33
Socio-cultural discourse analysis (Mercer et al OU)
bull Disputational talk characterised by disagreement and individualised decision making
bull Cumulative talk in which speakers build positively but uncritically on what the others have said
bull Exploratory talk in which partners engage critically but constructively with each others ideas
34 Mercer N (2004) Sociocultural discourse analysis analysing classroom talk as a social mode of thinking Journal of Applied Linguistics 1(2) 137-168
bull Exploratory talk in which partners engage critically but constructively with each others ideas
bull Statements and suggestions are offered for joint consideration
bull These may be challenged and counter-challenged but challenges are justified and alternative hypotheses are offered
bull Partners all actively participate and opinions are sought and considered before decisions are jointly made
bull Compared with the other two types in Exploratory talk knowledge is made more publicly accountable and reasoning is more visible in the talk
35
Socio-cultural discourse analysis (Mercer et al OU)
Mercer N (2004) Sociocultural discourse analysis analysing classroom talk as a social mode of thinking Journal of Applied Linguistics 1(2) 137-168
Analytics for identifying Exploratory talk
36
Elluminate sessions can be very long ndash lasting for hours or even covering days of a conference
It would be useful if we could identify where quality learning conversations seem to be taking place so we can recommend those sessions and not have to sit through online chat about virtual biscuits
Ferguson R and Buckingham Shum S Learning analytics to identify exploratory dialogue within synchronous text chat 1st International Conference on Learning Analytics amp Knowledge (Banff Canada 27 Mar-1 Apr 2011)
Defining indicators of Exploratory Talk
37
Category Indicator Challenge But if have to respond my view Critique However Irsquom not sure maybe Discussion of resources
Have you read more links
Evaluation Good example good point Explanation Means that our goals Explicit reasoning Next step relates to thatrsquos why Justification I mean we learned we observed Reflections of perspectives of others
Agree here is another makes the point take your point your view
Extract classified as Exploratory Talk
38
Time Contribution 242 PM I hate talking -P My question was whether gadgets were just
basically widgets and we could embed them in various web sites like Netvibes Google Desktop etc
242 PM Thanks thats great I am sure I understood everything but looks inspiring
243 PM Yes why OU tools not generic tools
243 PM Issues of interoperability
243 PM The new SocialLearn site looks a lot like a corkboard where you can add various widgets similar to those existing web start pages
243 PM What if we end up with as many appsgadgets as we have social networks and then we need a recommender for the apps
243 PM My question was on the definition of the crowd in the wisdom of crowds we acsess in the service model
243 PM there are various different flavours of widget eg Google gadgets W3C widgets etc SocialLearn has gone for Google gadgets
-60
-40
-20
0
20
40
60
80
92
8 9
32
93
6
94
0
94
1
94
6
95
0
95
3
95
6
10
00
10
05
10
07
10
07
10
09
10
13
10
17
10
23
10
27
10
31
10
35
10
40
10
45
10
52
10
55
11
04
11
08
11
11
11
17
11
20
11
24
11
26
11
28
11
31
11
32
11
35
11
36
11
38
11
39
11
41
11
44
11
46
11
48
11
52
11
54
12
00
12
03
12
04
12
05
Average Exploratory
Discourse analytics on webinar textchat
Sheffield UK not as sunny as yesterday - still warm Greetings from Hong Kong Morning from Wiltshire sunny here
See you bye for now bye and thank you Bye all for now
Extensions to Ferguson R and Buckingham Shum S (2011) Learning Analytics to Identify Exploratory Dialogue within Synchronous Text Chat Proc 1st Int Conf Learning Analytics amp Knowledge Feb 27-Mar 1 2011 Banff ACM Press Eprint httporoopenacuk28955
Given a 25 hour webinar where in the live textchat were the most effective learning conversations Not at the start and end of a webinar but if we zoom in on a peakhellip
Discourse analytics on webinar textchat
-100
-50
0
50
100
92
8 9
40
9
50
1
000
1
007
1
017
1
031
1
045
1
104
1
117
1
126
1
132
1
138
1
144
1
152
1
203
Averag
Wei amp He extensions to Ferguson R and Buckingham Shum S (2011) Learning Analytics to Identify Exploratory Dialogue within Synchronous Text Chat Proc 1st Int Conf Learning Analytics amp Knowledge Feb 27-Mar 1 2011 Banff ACM Press Eprint httporoopenacuk28955
Classified as ldquoexploratory
talkrdquo
(more substantive for learning)
ldquonon-exploratoryrdquo
Given a 25 hour webinar where in the live textchat were the most effective learning conversations
De Liddo A Buckingham Shum S Quinto I Bachler M and Cannavacciuolo L Discourse-centric learning analytics 1st International Conference on Learning Analytics amp Knowledge (Banff 27 Mar-1 Apr 2011) httporoopenacuk25829
KMirsquos Cohere a web deliberation platform enabling semantic social network and discourse network analytics
Rebecca is playing the role of broker
connecting 2 peersrsquo contributions in meaningful ways
analytics for scholarly writing
42
Discourse analysis (Xerox Incremental Parser)
BACKGROUND KNOWLEDGE
Recent studies indicate hellip
hellip the previously proposed hellip
hellip is universally accepted
NOVELTY
new insights provide direct evidence
we suggest a new approach
results define a novel role
OPEN QUESTION hellip little is known hellip hellip role hellip has been elusive
Current data is insufficient hellip
GENERALIZING emerging as a promising approach Our understanding has grown exponentially growing recognition of the
importance
CONRASTING IDEAS hellip unorthodox view resolves hellip paradoxes hellip
In contrast with previous hypotheses
inconsistent with past findings
SIGNIFICANCE studies have provided important advances
Knowledge is crucial for understanding
valuable information from studies
SURPRISE We have recently observed surprisingly
We have identified unusual The recent discovery suggests intriguing roles
SUMMARIZING The goal of this study Here we show
Altogether our results indicate
Detection of salient sentences in scholarly reports based on the rhetorical signals authors use
Aacutegnes Saacutendor amp OLnet Project httpolnetorgnode512
De Liddo A Saacutendor Aacute and Buckingham Shum S Contested Collective Intelligence Rationale Technologies and a Human-Machine Annotation Study Computer Supported Cooperative Work 21 4-5 (2012) 417-448 httporoopenacuk31052
Human and machine analysis of a text for key contributions
19 sentences annotated 22 sentences annotated 11 sentences same as human annotation
71 sentences annotated 59 sentences annotated 42 sentences same as human annotation
Document 1
Document 2
httptechnologieskmiopenacukcohere20120109cohere-plus-automated-rhetorical-annotation De Liddo A Saacutendor Aacute and Buckingham Shum S Contested Collective Intelligence Rationale Technologies and a Human-Machine Annotation Study Computer Supported Cooperative Work 21 4-5 (2012) 417-448 httporoopenacuk31052
analytics for intepersonal networking
45
Semantic Social Network Analytics
De Liddo A Buckingham Shum S Quinto I Bachler M and Cannavacciuolo L Discourse-centric learning analytics 1st International Conference on Learning Analytics amp Knowledge (Banff 27 Mar-1 Apr 2011) httporoopenacuk25829
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Closing thoughts
54
55
ldquoThe basic question is not what can we measure
The basic question is
what does a good education look likerdquo
(Gardner Campbell)
httpchroniclecomblogstechtherapy20120502episode-95-learning-analytics-could-lead-to-wal-martification-of-college httplak12wikispacescomRecordings
56
Our analytics promote values pedagogy and assessment regimes
Are we clear which master
our analytics serve Are we happy to be judged by them
57
Will learning analytics merely turbocharge the current educational paradigm
mdash which is so often declared
not fit for purposehellip
58
hellipor will learning analytics reflect what we now know about designing authentic
engaged learning developing the new qualities that a
complex society demands
the planhellip
joined-up multi-layer analytics an analytics ecosystem
are analytics (r)evolutionary
3
the convergence of analytics layers
4
Micro individual user actions
(and hence cohort)
MacroMesoMicro Learning Analytics
Meso institution-wide
Macro regionstatenationalinternational
Micro individual user actions
(and hence cohort)
MacroMesoMicro Learning Analytics
Meso institution-wide
Macro regionstatenationalinternational
Will institutions be dazzled by the dashboards or know what
questions to ask at each level
For examples of each level of analytichellip
7 Buckingham Shum S 2012 Our Learning Analytics are Our Pedagogy Keynote Address Expanding Horizons 2012 Conference Macquarie University Sydney httpwwwslidesharenetsbsour-learning-analytics-are-our-pedagogy
The VLEmdashBImdashITS convergence
8
Micro individual user actions
(and hence cohort)
As data migrates up it enriches higher layers normally accustomed to sparse data
Meso institution-wide
Macro regionstatenationalinternational
Aggregation of user traces enriches meso + macro analytics with finer-grained process data
Micro individual user actions
(and hence cohort)
hellipwhich in turn could enrich lower layers mdash local patterns can be cross-validated
Meso institution-wide
Macro regionstatenationalinternational
Aggregation of user traces enriches meso + macro analytics with finer-grained process data
Breadth + depth from macro + meso levels could add power to micro-analytics
anatomy of an analytics ecosystem
11
A learning analytics ecosystem
12
learners
educators
A learning analytics ecosystem
13
learners
educators
A learning analytics ecosystem
14
learners
educators
A learning analytics ecosystem
15
learners
educators
data curators translators
dashboard design team
data capture design team
Where did the data come from
16
learners
Where did the data come from
17
learners
researchers educators instructional designers
theories pedagogies
assessments tools
Where did the data come from
18
learners
researchers educators instructional designers
theories pedagogies
assessments tools
technologists
Data Intent
The map is not the territory Analytics are not the end but a means The goal is to optimize the whole system
19
learners
researchers educators instructional designers
theories pedagogies
assessments tools
desi
gn feedback
intent
outcome
Optimize the system for what
20
Same outcomes but higher scores
Learning Analytics as
Evolutionary Technology
bull more engaging bull better assessed bull better outcomes
bull deliverable at scale
21
New outcomes we couldnrsquot assess before
Learning Analytics as
Revolutionary Technology
bull learner behaviours quantifiable bull interpersonal networks quantifiable
bull discourse quantifiable bull moods and dispositions quantifiable
22
ldquoWe are preparing students for jobs that do not exist yet that will use technologies that have not been invented yet in order to solve problems that are not even problems yetrdquo
ldquoShift Happensrdquo httpshifthappenswikispacescom
23
Learning analytics for this
Learning analytics for this
ldquoWhile employers continue to demand high academic standards they also now want more They want people who can adapt see connections innovate communicate and work with others This is true in many areas of work The new knowledge-based economies in particular will increasingly depend on these abilities Many businesses are paying for courses to promote creative abilities to teach the skills and attitudes that are now essential for economic successhelliprdquo
All our Futures Creativity culture amp education May 1999 24
Learning analytics for this
25
Creativity Culture and Education (2009) Changing Young Lives 2012 Newcastle CCE httpwwwcreativitycultureeducationorgchanging-young-lives-2012
Think about the analytics products and initiatives reviewed above ndash where would you locate them on these dimensions
Expert-led enquiry
Student-led enquiry
Expert-led teaching
Student-led revision
Kno
wle
dge
co
-gen
erat
ion
an
d us
e
Pre-
scrib
ed
Kno
wle
dge
Teacher agency Student agency
Repetition Abstraction Acquisition
Authenticity Agency Identity
Teaching as learning design
The Knowledge-Agency Window
Learning analytics for this
Ruth Deakin Crick Univ Bristol Centre for Systems Learning amp Leadership ldquoPedagogy of Hoperdquo httplearningemergencenet20120921pedagogy-of-hope
analytics grounded in the principles of good
assessment for learning
(not summative assessment for
grading pupils teachers institutions or nations)
27
Assessment for Learning
28
httpassessment-reform-grouporg Few learning analytics are
currently able to take o board the richness of this
original conception of assessment for learning
Assessment for Learning
29
httpassessment-reform-grouporg
Assessment for Learning
30
httpassessment-reform-grouporg
To what extent could automated
feedback be designed and evaluated with
emotional impact in mind
Assessment for Learning
31
httpassessment-reform-grouporg
Can analytics identify proxies
for such advanced qualities
Assessment for Learning
32
httpassessment-reform-grouporg
How do we provide analytics feedback
that does not disempower and de-motivate struggling
learners
analytics for learning conversations
33
Socio-cultural discourse analysis (Mercer et al OU)
bull Disputational talk characterised by disagreement and individualised decision making
bull Cumulative talk in which speakers build positively but uncritically on what the others have said
bull Exploratory talk in which partners engage critically but constructively with each others ideas
34 Mercer N (2004) Sociocultural discourse analysis analysing classroom talk as a social mode of thinking Journal of Applied Linguistics 1(2) 137-168
bull Exploratory talk in which partners engage critically but constructively with each others ideas
bull Statements and suggestions are offered for joint consideration
bull These may be challenged and counter-challenged but challenges are justified and alternative hypotheses are offered
bull Partners all actively participate and opinions are sought and considered before decisions are jointly made
bull Compared with the other two types in Exploratory talk knowledge is made more publicly accountable and reasoning is more visible in the talk
35
Socio-cultural discourse analysis (Mercer et al OU)
Mercer N (2004) Sociocultural discourse analysis analysing classroom talk as a social mode of thinking Journal of Applied Linguistics 1(2) 137-168
Analytics for identifying Exploratory talk
36
Elluminate sessions can be very long ndash lasting for hours or even covering days of a conference
It would be useful if we could identify where quality learning conversations seem to be taking place so we can recommend those sessions and not have to sit through online chat about virtual biscuits
Ferguson R and Buckingham Shum S Learning analytics to identify exploratory dialogue within synchronous text chat 1st International Conference on Learning Analytics amp Knowledge (Banff Canada 27 Mar-1 Apr 2011)
Defining indicators of Exploratory Talk
37
Category Indicator Challenge But if have to respond my view Critique However Irsquom not sure maybe Discussion of resources
Have you read more links
Evaluation Good example good point Explanation Means that our goals Explicit reasoning Next step relates to thatrsquos why Justification I mean we learned we observed Reflections of perspectives of others
Agree here is another makes the point take your point your view
Extract classified as Exploratory Talk
38
Time Contribution 242 PM I hate talking -P My question was whether gadgets were just
basically widgets and we could embed them in various web sites like Netvibes Google Desktop etc
242 PM Thanks thats great I am sure I understood everything but looks inspiring
243 PM Yes why OU tools not generic tools
243 PM Issues of interoperability
243 PM The new SocialLearn site looks a lot like a corkboard where you can add various widgets similar to those existing web start pages
243 PM What if we end up with as many appsgadgets as we have social networks and then we need a recommender for the apps
243 PM My question was on the definition of the crowd in the wisdom of crowds we acsess in the service model
243 PM there are various different flavours of widget eg Google gadgets W3C widgets etc SocialLearn has gone for Google gadgets
-60
-40
-20
0
20
40
60
80
92
8 9
32
93
6
94
0
94
1
94
6
95
0
95
3
95
6
10
00
10
05
10
07
10
07
10
09
10
13
10
17
10
23
10
27
10
31
10
35
10
40
10
45
10
52
10
55
11
04
11
08
11
11
11
17
11
20
11
24
11
26
11
28
11
31
11
32
11
35
11
36
11
38
11
39
11
41
11
44
11
46
11
48
11
52
11
54
12
00
12
03
12
04
12
05
Average Exploratory
Discourse analytics on webinar textchat
Sheffield UK not as sunny as yesterday - still warm Greetings from Hong Kong Morning from Wiltshire sunny here
See you bye for now bye and thank you Bye all for now
Extensions to Ferguson R and Buckingham Shum S (2011) Learning Analytics to Identify Exploratory Dialogue within Synchronous Text Chat Proc 1st Int Conf Learning Analytics amp Knowledge Feb 27-Mar 1 2011 Banff ACM Press Eprint httporoopenacuk28955
Given a 25 hour webinar where in the live textchat were the most effective learning conversations Not at the start and end of a webinar but if we zoom in on a peakhellip
Discourse analytics on webinar textchat
-100
-50
0
50
100
92
8 9
40
9
50
1
000
1
007
1
017
1
031
1
045
1
104
1
117
1
126
1
132
1
138
1
144
1
152
1
203
Averag
Wei amp He extensions to Ferguson R and Buckingham Shum S (2011) Learning Analytics to Identify Exploratory Dialogue within Synchronous Text Chat Proc 1st Int Conf Learning Analytics amp Knowledge Feb 27-Mar 1 2011 Banff ACM Press Eprint httporoopenacuk28955
Classified as ldquoexploratory
talkrdquo
(more substantive for learning)
ldquonon-exploratoryrdquo
Given a 25 hour webinar where in the live textchat were the most effective learning conversations
De Liddo A Buckingham Shum S Quinto I Bachler M and Cannavacciuolo L Discourse-centric learning analytics 1st International Conference on Learning Analytics amp Knowledge (Banff 27 Mar-1 Apr 2011) httporoopenacuk25829
KMirsquos Cohere a web deliberation platform enabling semantic social network and discourse network analytics
Rebecca is playing the role of broker
connecting 2 peersrsquo contributions in meaningful ways
analytics for scholarly writing
42
Discourse analysis (Xerox Incremental Parser)
BACKGROUND KNOWLEDGE
Recent studies indicate hellip
hellip the previously proposed hellip
hellip is universally accepted
NOVELTY
new insights provide direct evidence
we suggest a new approach
results define a novel role
OPEN QUESTION hellip little is known hellip hellip role hellip has been elusive
Current data is insufficient hellip
GENERALIZING emerging as a promising approach Our understanding has grown exponentially growing recognition of the
importance
CONRASTING IDEAS hellip unorthodox view resolves hellip paradoxes hellip
In contrast with previous hypotheses
inconsistent with past findings
SIGNIFICANCE studies have provided important advances
Knowledge is crucial for understanding
valuable information from studies
SURPRISE We have recently observed surprisingly
We have identified unusual The recent discovery suggests intriguing roles
SUMMARIZING The goal of this study Here we show
Altogether our results indicate
Detection of salient sentences in scholarly reports based on the rhetorical signals authors use
Aacutegnes Saacutendor amp OLnet Project httpolnetorgnode512
De Liddo A Saacutendor Aacute and Buckingham Shum S Contested Collective Intelligence Rationale Technologies and a Human-Machine Annotation Study Computer Supported Cooperative Work 21 4-5 (2012) 417-448 httporoopenacuk31052
Human and machine analysis of a text for key contributions
19 sentences annotated 22 sentences annotated 11 sentences same as human annotation
71 sentences annotated 59 sentences annotated 42 sentences same as human annotation
Document 1
Document 2
httptechnologieskmiopenacukcohere20120109cohere-plus-automated-rhetorical-annotation De Liddo A Saacutendor Aacute and Buckingham Shum S Contested Collective Intelligence Rationale Technologies and a Human-Machine Annotation Study Computer Supported Cooperative Work 21 4-5 (2012) 417-448 httporoopenacuk31052
analytics for intepersonal networking
45
Semantic Social Network Analytics
De Liddo A Buckingham Shum S Quinto I Bachler M and Cannavacciuolo L Discourse-centric learning analytics 1st International Conference on Learning Analytics amp Knowledge (Banff 27 Mar-1 Apr 2011) httporoopenacuk25829
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Closing thoughts
54
55
ldquoThe basic question is not what can we measure
The basic question is
what does a good education look likerdquo
(Gardner Campbell)
httpchroniclecomblogstechtherapy20120502episode-95-learning-analytics-could-lead-to-wal-martification-of-college httplak12wikispacescomRecordings
56
Our analytics promote values pedagogy and assessment regimes
Are we clear which master
our analytics serve Are we happy to be judged by them
57
Will learning analytics merely turbocharge the current educational paradigm
mdash which is so often declared
not fit for purposehellip
58
hellipor will learning analytics reflect what we now know about designing authentic
engaged learning developing the new qualities that a
complex society demands
the convergence of analytics layers
4
Micro individual user actions
(and hence cohort)
MacroMesoMicro Learning Analytics
Meso institution-wide
Macro regionstatenationalinternational
Micro individual user actions
(and hence cohort)
MacroMesoMicro Learning Analytics
Meso institution-wide
Macro regionstatenationalinternational
Will institutions be dazzled by the dashboards or know what
questions to ask at each level
For examples of each level of analytichellip
7 Buckingham Shum S 2012 Our Learning Analytics are Our Pedagogy Keynote Address Expanding Horizons 2012 Conference Macquarie University Sydney httpwwwslidesharenetsbsour-learning-analytics-are-our-pedagogy
The VLEmdashBImdashITS convergence
8
Micro individual user actions
(and hence cohort)
As data migrates up it enriches higher layers normally accustomed to sparse data
Meso institution-wide
Macro regionstatenationalinternational
Aggregation of user traces enriches meso + macro analytics with finer-grained process data
Micro individual user actions
(and hence cohort)
hellipwhich in turn could enrich lower layers mdash local patterns can be cross-validated
Meso institution-wide
Macro regionstatenationalinternational
Aggregation of user traces enriches meso + macro analytics with finer-grained process data
Breadth + depth from macro + meso levels could add power to micro-analytics
anatomy of an analytics ecosystem
11
A learning analytics ecosystem
12
learners
educators
A learning analytics ecosystem
13
learners
educators
A learning analytics ecosystem
14
learners
educators
A learning analytics ecosystem
15
learners
educators
data curators translators
dashboard design team
data capture design team
Where did the data come from
16
learners
Where did the data come from
17
learners
researchers educators instructional designers
theories pedagogies
assessments tools
Where did the data come from
18
learners
researchers educators instructional designers
theories pedagogies
assessments tools
technologists
Data Intent
The map is not the territory Analytics are not the end but a means The goal is to optimize the whole system
19
learners
researchers educators instructional designers
theories pedagogies
assessments tools
desi
gn feedback
intent
outcome
Optimize the system for what
20
Same outcomes but higher scores
Learning Analytics as
Evolutionary Technology
bull more engaging bull better assessed bull better outcomes
bull deliverable at scale
21
New outcomes we couldnrsquot assess before
Learning Analytics as
Revolutionary Technology
bull learner behaviours quantifiable bull interpersonal networks quantifiable
bull discourse quantifiable bull moods and dispositions quantifiable
22
ldquoWe are preparing students for jobs that do not exist yet that will use technologies that have not been invented yet in order to solve problems that are not even problems yetrdquo
ldquoShift Happensrdquo httpshifthappenswikispacescom
23
Learning analytics for this
Learning analytics for this
ldquoWhile employers continue to demand high academic standards they also now want more They want people who can adapt see connections innovate communicate and work with others This is true in many areas of work The new knowledge-based economies in particular will increasingly depend on these abilities Many businesses are paying for courses to promote creative abilities to teach the skills and attitudes that are now essential for economic successhelliprdquo
All our Futures Creativity culture amp education May 1999 24
Learning analytics for this
25
Creativity Culture and Education (2009) Changing Young Lives 2012 Newcastle CCE httpwwwcreativitycultureeducationorgchanging-young-lives-2012
Think about the analytics products and initiatives reviewed above ndash where would you locate them on these dimensions
Expert-led enquiry
Student-led enquiry
Expert-led teaching
Student-led revision
Kno
wle
dge
co
-gen
erat
ion
an
d us
e
Pre-
scrib
ed
Kno
wle
dge
Teacher agency Student agency
Repetition Abstraction Acquisition
Authenticity Agency Identity
Teaching as learning design
The Knowledge-Agency Window
Learning analytics for this
Ruth Deakin Crick Univ Bristol Centre for Systems Learning amp Leadership ldquoPedagogy of Hoperdquo httplearningemergencenet20120921pedagogy-of-hope
analytics grounded in the principles of good
assessment for learning
(not summative assessment for
grading pupils teachers institutions or nations)
27
Assessment for Learning
28
httpassessment-reform-grouporg Few learning analytics are
currently able to take o board the richness of this
original conception of assessment for learning
Assessment for Learning
29
httpassessment-reform-grouporg
Assessment for Learning
30
httpassessment-reform-grouporg
To what extent could automated
feedback be designed and evaluated with
emotional impact in mind
Assessment for Learning
31
httpassessment-reform-grouporg
Can analytics identify proxies
for such advanced qualities
Assessment for Learning
32
httpassessment-reform-grouporg
How do we provide analytics feedback
that does not disempower and de-motivate struggling
learners
analytics for learning conversations
33
Socio-cultural discourse analysis (Mercer et al OU)
bull Disputational talk characterised by disagreement and individualised decision making
bull Cumulative talk in which speakers build positively but uncritically on what the others have said
bull Exploratory talk in which partners engage critically but constructively with each others ideas
34 Mercer N (2004) Sociocultural discourse analysis analysing classroom talk as a social mode of thinking Journal of Applied Linguistics 1(2) 137-168
bull Exploratory talk in which partners engage critically but constructively with each others ideas
bull Statements and suggestions are offered for joint consideration
bull These may be challenged and counter-challenged but challenges are justified and alternative hypotheses are offered
bull Partners all actively participate and opinions are sought and considered before decisions are jointly made
bull Compared with the other two types in Exploratory talk knowledge is made more publicly accountable and reasoning is more visible in the talk
35
Socio-cultural discourse analysis (Mercer et al OU)
Mercer N (2004) Sociocultural discourse analysis analysing classroom talk as a social mode of thinking Journal of Applied Linguistics 1(2) 137-168
Analytics for identifying Exploratory talk
36
Elluminate sessions can be very long ndash lasting for hours or even covering days of a conference
It would be useful if we could identify where quality learning conversations seem to be taking place so we can recommend those sessions and not have to sit through online chat about virtual biscuits
Ferguson R and Buckingham Shum S Learning analytics to identify exploratory dialogue within synchronous text chat 1st International Conference on Learning Analytics amp Knowledge (Banff Canada 27 Mar-1 Apr 2011)
Defining indicators of Exploratory Talk
37
Category Indicator Challenge But if have to respond my view Critique However Irsquom not sure maybe Discussion of resources
Have you read more links
Evaluation Good example good point Explanation Means that our goals Explicit reasoning Next step relates to thatrsquos why Justification I mean we learned we observed Reflections of perspectives of others
Agree here is another makes the point take your point your view
Extract classified as Exploratory Talk
38
Time Contribution 242 PM I hate talking -P My question was whether gadgets were just
basically widgets and we could embed them in various web sites like Netvibes Google Desktop etc
242 PM Thanks thats great I am sure I understood everything but looks inspiring
243 PM Yes why OU tools not generic tools
243 PM Issues of interoperability
243 PM The new SocialLearn site looks a lot like a corkboard where you can add various widgets similar to those existing web start pages
243 PM What if we end up with as many appsgadgets as we have social networks and then we need a recommender for the apps
243 PM My question was on the definition of the crowd in the wisdom of crowds we acsess in the service model
243 PM there are various different flavours of widget eg Google gadgets W3C widgets etc SocialLearn has gone for Google gadgets
-60
-40
-20
0
20
40
60
80
92
8 9
32
93
6
94
0
94
1
94
6
95
0
95
3
95
6
10
00
10
05
10
07
10
07
10
09
10
13
10
17
10
23
10
27
10
31
10
35
10
40
10
45
10
52
10
55
11
04
11
08
11
11
11
17
11
20
11
24
11
26
11
28
11
31
11
32
11
35
11
36
11
38
11
39
11
41
11
44
11
46
11
48
11
52
11
54
12
00
12
03
12
04
12
05
Average Exploratory
Discourse analytics on webinar textchat
Sheffield UK not as sunny as yesterday - still warm Greetings from Hong Kong Morning from Wiltshire sunny here
See you bye for now bye and thank you Bye all for now
Extensions to Ferguson R and Buckingham Shum S (2011) Learning Analytics to Identify Exploratory Dialogue within Synchronous Text Chat Proc 1st Int Conf Learning Analytics amp Knowledge Feb 27-Mar 1 2011 Banff ACM Press Eprint httporoopenacuk28955
Given a 25 hour webinar where in the live textchat were the most effective learning conversations Not at the start and end of a webinar but if we zoom in on a peakhellip
Discourse analytics on webinar textchat
-100
-50
0
50
100
92
8 9
40
9
50
1
000
1
007
1
017
1
031
1
045
1
104
1
117
1
126
1
132
1
138
1
144
1
152
1
203
Averag
Wei amp He extensions to Ferguson R and Buckingham Shum S (2011) Learning Analytics to Identify Exploratory Dialogue within Synchronous Text Chat Proc 1st Int Conf Learning Analytics amp Knowledge Feb 27-Mar 1 2011 Banff ACM Press Eprint httporoopenacuk28955
Classified as ldquoexploratory
talkrdquo
(more substantive for learning)
ldquonon-exploratoryrdquo
Given a 25 hour webinar where in the live textchat were the most effective learning conversations
De Liddo A Buckingham Shum S Quinto I Bachler M and Cannavacciuolo L Discourse-centric learning analytics 1st International Conference on Learning Analytics amp Knowledge (Banff 27 Mar-1 Apr 2011) httporoopenacuk25829
KMirsquos Cohere a web deliberation platform enabling semantic social network and discourse network analytics
Rebecca is playing the role of broker
connecting 2 peersrsquo contributions in meaningful ways
analytics for scholarly writing
42
Discourse analysis (Xerox Incremental Parser)
BACKGROUND KNOWLEDGE
Recent studies indicate hellip
hellip the previously proposed hellip
hellip is universally accepted
NOVELTY
new insights provide direct evidence
we suggest a new approach
results define a novel role
OPEN QUESTION hellip little is known hellip hellip role hellip has been elusive
Current data is insufficient hellip
GENERALIZING emerging as a promising approach Our understanding has grown exponentially growing recognition of the
importance
CONRASTING IDEAS hellip unorthodox view resolves hellip paradoxes hellip
In contrast with previous hypotheses
inconsistent with past findings
SIGNIFICANCE studies have provided important advances
Knowledge is crucial for understanding
valuable information from studies
SURPRISE We have recently observed surprisingly
We have identified unusual The recent discovery suggests intriguing roles
SUMMARIZING The goal of this study Here we show
Altogether our results indicate
Detection of salient sentences in scholarly reports based on the rhetorical signals authors use
Aacutegnes Saacutendor amp OLnet Project httpolnetorgnode512
De Liddo A Saacutendor Aacute and Buckingham Shum S Contested Collective Intelligence Rationale Technologies and a Human-Machine Annotation Study Computer Supported Cooperative Work 21 4-5 (2012) 417-448 httporoopenacuk31052
Human and machine analysis of a text for key contributions
19 sentences annotated 22 sentences annotated 11 sentences same as human annotation
71 sentences annotated 59 sentences annotated 42 sentences same as human annotation
Document 1
Document 2
httptechnologieskmiopenacukcohere20120109cohere-plus-automated-rhetorical-annotation De Liddo A Saacutendor Aacute and Buckingham Shum S Contested Collective Intelligence Rationale Technologies and a Human-Machine Annotation Study Computer Supported Cooperative Work 21 4-5 (2012) 417-448 httporoopenacuk31052
analytics for intepersonal networking
45
Semantic Social Network Analytics
De Liddo A Buckingham Shum S Quinto I Bachler M and Cannavacciuolo L Discourse-centric learning analytics 1st International Conference on Learning Analytics amp Knowledge (Banff 27 Mar-1 Apr 2011) httporoopenacuk25829
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Closing thoughts
54
55
ldquoThe basic question is not what can we measure
The basic question is
what does a good education look likerdquo
(Gardner Campbell)
httpchroniclecomblogstechtherapy20120502episode-95-learning-analytics-could-lead-to-wal-martification-of-college httplak12wikispacescomRecordings
56
Our analytics promote values pedagogy and assessment regimes
Are we clear which master
our analytics serve Are we happy to be judged by them
57
Will learning analytics merely turbocharge the current educational paradigm
mdash which is so often declared
not fit for purposehellip
58
hellipor will learning analytics reflect what we now know about designing authentic
engaged learning developing the new qualities that a
complex society demands
Micro individual user actions
(and hence cohort)
MacroMesoMicro Learning Analytics
Meso institution-wide
Macro regionstatenationalinternational
Micro individual user actions
(and hence cohort)
MacroMesoMicro Learning Analytics
Meso institution-wide
Macro regionstatenationalinternational
Will institutions be dazzled by the dashboards or know what
questions to ask at each level
For examples of each level of analytichellip
7 Buckingham Shum S 2012 Our Learning Analytics are Our Pedagogy Keynote Address Expanding Horizons 2012 Conference Macquarie University Sydney httpwwwslidesharenetsbsour-learning-analytics-are-our-pedagogy
The VLEmdashBImdashITS convergence
8
Micro individual user actions
(and hence cohort)
As data migrates up it enriches higher layers normally accustomed to sparse data
Meso institution-wide
Macro regionstatenationalinternational
Aggregation of user traces enriches meso + macro analytics with finer-grained process data
Micro individual user actions
(and hence cohort)
hellipwhich in turn could enrich lower layers mdash local patterns can be cross-validated
Meso institution-wide
Macro regionstatenationalinternational
Aggregation of user traces enriches meso + macro analytics with finer-grained process data
Breadth + depth from macro + meso levels could add power to micro-analytics
anatomy of an analytics ecosystem
11
A learning analytics ecosystem
12
learners
educators
A learning analytics ecosystem
13
learners
educators
A learning analytics ecosystem
14
learners
educators
A learning analytics ecosystem
15
learners
educators
data curators translators
dashboard design team
data capture design team
Where did the data come from
16
learners
Where did the data come from
17
learners
researchers educators instructional designers
theories pedagogies
assessments tools
Where did the data come from
18
learners
researchers educators instructional designers
theories pedagogies
assessments tools
technologists
Data Intent
The map is not the territory Analytics are not the end but a means The goal is to optimize the whole system
19
learners
researchers educators instructional designers
theories pedagogies
assessments tools
desi
gn feedback
intent
outcome
Optimize the system for what
20
Same outcomes but higher scores
Learning Analytics as
Evolutionary Technology
bull more engaging bull better assessed bull better outcomes
bull deliverable at scale
21
New outcomes we couldnrsquot assess before
Learning Analytics as
Revolutionary Technology
bull learner behaviours quantifiable bull interpersonal networks quantifiable
bull discourse quantifiable bull moods and dispositions quantifiable
22
ldquoWe are preparing students for jobs that do not exist yet that will use technologies that have not been invented yet in order to solve problems that are not even problems yetrdquo
ldquoShift Happensrdquo httpshifthappenswikispacescom
23
Learning analytics for this
Learning analytics for this
ldquoWhile employers continue to demand high academic standards they also now want more They want people who can adapt see connections innovate communicate and work with others This is true in many areas of work The new knowledge-based economies in particular will increasingly depend on these abilities Many businesses are paying for courses to promote creative abilities to teach the skills and attitudes that are now essential for economic successhelliprdquo
All our Futures Creativity culture amp education May 1999 24
Learning analytics for this
25
Creativity Culture and Education (2009) Changing Young Lives 2012 Newcastle CCE httpwwwcreativitycultureeducationorgchanging-young-lives-2012
Think about the analytics products and initiatives reviewed above ndash where would you locate them on these dimensions
Expert-led enquiry
Student-led enquiry
Expert-led teaching
Student-led revision
Kno
wle
dge
co
-gen
erat
ion
an
d us
e
Pre-
scrib
ed
Kno
wle
dge
Teacher agency Student agency
Repetition Abstraction Acquisition
Authenticity Agency Identity
Teaching as learning design
The Knowledge-Agency Window
Learning analytics for this
Ruth Deakin Crick Univ Bristol Centre for Systems Learning amp Leadership ldquoPedagogy of Hoperdquo httplearningemergencenet20120921pedagogy-of-hope
analytics grounded in the principles of good
assessment for learning
(not summative assessment for
grading pupils teachers institutions or nations)
27
Assessment for Learning
28
httpassessment-reform-grouporg Few learning analytics are
currently able to take o board the richness of this
original conception of assessment for learning
Assessment for Learning
29
httpassessment-reform-grouporg
Assessment for Learning
30
httpassessment-reform-grouporg
To what extent could automated
feedback be designed and evaluated with
emotional impact in mind
Assessment for Learning
31
httpassessment-reform-grouporg
Can analytics identify proxies
for such advanced qualities
Assessment for Learning
32
httpassessment-reform-grouporg
How do we provide analytics feedback
that does not disempower and de-motivate struggling
learners
analytics for learning conversations
33
Socio-cultural discourse analysis (Mercer et al OU)
bull Disputational talk characterised by disagreement and individualised decision making
bull Cumulative talk in which speakers build positively but uncritically on what the others have said
bull Exploratory talk in which partners engage critically but constructively with each others ideas
34 Mercer N (2004) Sociocultural discourse analysis analysing classroom talk as a social mode of thinking Journal of Applied Linguistics 1(2) 137-168
bull Exploratory talk in which partners engage critically but constructively with each others ideas
bull Statements and suggestions are offered for joint consideration
bull These may be challenged and counter-challenged but challenges are justified and alternative hypotheses are offered
bull Partners all actively participate and opinions are sought and considered before decisions are jointly made
bull Compared with the other two types in Exploratory talk knowledge is made more publicly accountable and reasoning is more visible in the talk
35
Socio-cultural discourse analysis (Mercer et al OU)
Mercer N (2004) Sociocultural discourse analysis analysing classroom talk as a social mode of thinking Journal of Applied Linguistics 1(2) 137-168
Analytics for identifying Exploratory talk
36
Elluminate sessions can be very long ndash lasting for hours or even covering days of a conference
It would be useful if we could identify where quality learning conversations seem to be taking place so we can recommend those sessions and not have to sit through online chat about virtual biscuits
Ferguson R and Buckingham Shum S Learning analytics to identify exploratory dialogue within synchronous text chat 1st International Conference on Learning Analytics amp Knowledge (Banff Canada 27 Mar-1 Apr 2011)
Defining indicators of Exploratory Talk
37
Category Indicator Challenge But if have to respond my view Critique However Irsquom not sure maybe Discussion of resources
Have you read more links
Evaluation Good example good point Explanation Means that our goals Explicit reasoning Next step relates to thatrsquos why Justification I mean we learned we observed Reflections of perspectives of others
Agree here is another makes the point take your point your view
Extract classified as Exploratory Talk
38
Time Contribution 242 PM I hate talking -P My question was whether gadgets were just
basically widgets and we could embed them in various web sites like Netvibes Google Desktop etc
242 PM Thanks thats great I am sure I understood everything but looks inspiring
243 PM Yes why OU tools not generic tools
243 PM Issues of interoperability
243 PM The new SocialLearn site looks a lot like a corkboard where you can add various widgets similar to those existing web start pages
243 PM What if we end up with as many appsgadgets as we have social networks and then we need a recommender for the apps
243 PM My question was on the definition of the crowd in the wisdom of crowds we acsess in the service model
243 PM there are various different flavours of widget eg Google gadgets W3C widgets etc SocialLearn has gone for Google gadgets
-60
-40
-20
0
20
40
60
80
92
8 9
32
93
6
94
0
94
1
94
6
95
0
95
3
95
6
10
00
10
05
10
07
10
07
10
09
10
13
10
17
10
23
10
27
10
31
10
35
10
40
10
45
10
52
10
55
11
04
11
08
11
11
11
17
11
20
11
24
11
26
11
28
11
31
11
32
11
35
11
36
11
38
11
39
11
41
11
44
11
46
11
48
11
52
11
54
12
00
12
03
12
04
12
05
Average Exploratory
Discourse analytics on webinar textchat
Sheffield UK not as sunny as yesterday - still warm Greetings from Hong Kong Morning from Wiltshire sunny here
See you bye for now bye and thank you Bye all for now
Extensions to Ferguson R and Buckingham Shum S (2011) Learning Analytics to Identify Exploratory Dialogue within Synchronous Text Chat Proc 1st Int Conf Learning Analytics amp Knowledge Feb 27-Mar 1 2011 Banff ACM Press Eprint httporoopenacuk28955
Given a 25 hour webinar where in the live textchat were the most effective learning conversations Not at the start and end of a webinar but if we zoom in on a peakhellip
Discourse analytics on webinar textchat
-100
-50
0
50
100
92
8 9
40
9
50
1
000
1
007
1
017
1
031
1
045
1
104
1
117
1
126
1
132
1
138
1
144
1
152
1
203
Averag
Wei amp He extensions to Ferguson R and Buckingham Shum S (2011) Learning Analytics to Identify Exploratory Dialogue within Synchronous Text Chat Proc 1st Int Conf Learning Analytics amp Knowledge Feb 27-Mar 1 2011 Banff ACM Press Eprint httporoopenacuk28955
Classified as ldquoexploratory
talkrdquo
(more substantive for learning)
ldquonon-exploratoryrdquo
Given a 25 hour webinar where in the live textchat were the most effective learning conversations
De Liddo A Buckingham Shum S Quinto I Bachler M and Cannavacciuolo L Discourse-centric learning analytics 1st International Conference on Learning Analytics amp Knowledge (Banff 27 Mar-1 Apr 2011) httporoopenacuk25829
KMirsquos Cohere a web deliberation platform enabling semantic social network and discourse network analytics
Rebecca is playing the role of broker
connecting 2 peersrsquo contributions in meaningful ways
analytics for scholarly writing
42
Discourse analysis (Xerox Incremental Parser)
BACKGROUND KNOWLEDGE
Recent studies indicate hellip
hellip the previously proposed hellip
hellip is universally accepted
NOVELTY
new insights provide direct evidence
we suggest a new approach
results define a novel role
OPEN QUESTION hellip little is known hellip hellip role hellip has been elusive
Current data is insufficient hellip
GENERALIZING emerging as a promising approach Our understanding has grown exponentially growing recognition of the
importance
CONRASTING IDEAS hellip unorthodox view resolves hellip paradoxes hellip
In contrast with previous hypotheses
inconsistent with past findings
SIGNIFICANCE studies have provided important advances
Knowledge is crucial for understanding
valuable information from studies
SURPRISE We have recently observed surprisingly
We have identified unusual The recent discovery suggests intriguing roles
SUMMARIZING The goal of this study Here we show
Altogether our results indicate
Detection of salient sentences in scholarly reports based on the rhetorical signals authors use
Aacutegnes Saacutendor amp OLnet Project httpolnetorgnode512
De Liddo A Saacutendor Aacute and Buckingham Shum S Contested Collective Intelligence Rationale Technologies and a Human-Machine Annotation Study Computer Supported Cooperative Work 21 4-5 (2012) 417-448 httporoopenacuk31052
Human and machine analysis of a text for key contributions
19 sentences annotated 22 sentences annotated 11 sentences same as human annotation
71 sentences annotated 59 sentences annotated 42 sentences same as human annotation
Document 1
Document 2
httptechnologieskmiopenacukcohere20120109cohere-plus-automated-rhetorical-annotation De Liddo A Saacutendor Aacute and Buckingham Shum S Contested Collective Intelligence Rationale Technologies and a Human-Machine Annotation Study Computer Supported Cooperative Work 21 4-5 (2012) 417-448 httporoopenacuk31052
analytics for intepersonal networking
45
Semantic Social Network Analytics
De Liddo A Buckingham Shum S Quinto I Bachler M and Cannavacciuolo L Discourse-centric learning analytics 1st International Conference on Learning Analytics amp Knowledge (Banff 27 Mar-1 Apr 2011) httporoopenacuk25829
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Closing thoughts
54
55
ldquoThe basic question is not what can we measure
The basic question is
what does a good education look likerdquo
(Gardner Campbell)
httpchroniclecomblogstechtherapy20120502episode-95-learning-analytics-could-lead-to-wal-martification-of-college httplak12wikispacescomRecordings
56
Our analytics promote values pedagogy and assessment regimes
Are we clear which master
our analytics serve Are we happy to be judged by them
57
Will learning analytics merely turbocharge the current educational paradigm
mdash which is so often declared
not fit for purposehellip
58
hellipor will learning analytics reflect what we now know about designing authentic
engaged learning developing the new qualities that a
complex society demands
Micro individual user actions
(and hence cohort)
MacroMesoMicro Learning Analytics
Meso institution-wide
Macro regionstatenationalinternational
Will institutions be dazzled by the dashboards or know what
questions to ask at each level
For examples of each level of analytichellip
7 Buckingham Shum S 2012 Our Learning Analytics are Our Pedagogy Keynote Address Expanding Horizons 2012 Conference Macquarie University Sydney httpwwwslidesharenetsbsour-learning-analytics-are-our-pedagogy
The VLEmdashBImdashITS convergence
8
Micro individual user actions
(and hence cohort)
As data migrates up it enriches higher layers normally accustomed to sparse data
Meso institution-wide
Macro regionstatenationalinternational
Aggregation of user traces enriches meso + macro analytics with finer-grained process data
Micro individual user actions
(and hence cohort)
hellipwhich in turn could enrich lower layers mdash local patterns can be cross-validated
Meso institution-wide
Macro regionstatenationalinternational
Aggregation of user traces enriches meso + macro analytics with finer-grained process data
Breadth + depth from macro + meso levels could add power to micro-analytics
anatomy of an analytics ecosystem
11
A learning analytics ecosystem
12
learners
educators
A learning analytics ecosystem
13
learners
educators
A learning analytics ecosystem
14
learners
educators
A learning analytics ecosystem
15
learners
educators
data curators translators
dashboard design team
data capture design team
Where did the data come from
16
learners
Where did the data come from
17
learners
researchers educators instructional designers
theories pedagogies
assessments tools
Where did the data come from
18
learners
researchers educators instructional designers
theories pedagogies
assessments tools
technologists
Data Intent
The map is not the territory Analytics are not the end but a means The goal is to optimize the whole system
19
learners
researchers educators instructional designers
theories pedagogies
assessments tools
desi
gn feedback
intent
outcome
Optimize the system for what
20
Same outcomes but higher scores
Learning Analytics as
Evolutionary Technology
bull more engaging bull better assessed bull better outcomes
bull deliverable at scale
21
New outcomes we couldnrsquot assess before
Learning Analytics as
Revolutionary Technology
bull learner behaviours quantifiable bull interpersonal networks quantifiable
bull discourse quantifiable bull moods and dispositions quantifiable
22
ldquoWe are preparing students for jobs that do not exist yet that will use technologies that have not been invented yet in order to solve problems that are not even problems yetrdquo
ldquoShift Happensrdquo httpshifthappenswikispacescom
23
Learning analytics for this
Learning analytics for this
ldquoWhile employers continue to demand high academic standards they also now want more They want people who can adapt see connections innovate communicate and work with others This is true in many areas of work The new knowledge-based economies in particular will increasingly depend on these abilities Many businesses are paying for courses to promote creative abilities to teach the skills and attitudes that are now essential for economic successhelliprdquo
All our Futures Creativity culture amp education May 1999 24
Learning analytics for this
25
Creativity Culture and Education (2009) Changing Young Lives 2012 Newcastle CCE httpwwwcreativitycultureeducationorgchanging-young-lives-2012
Think about the analytics products and initiatives reviewed above ndash where would you locate them on these dimensions
Expert-led enquiry
Student-led enquiry
Expert-led teaching
Student-led revision
Kno
wle
dge
co
-gen
erat
ion
an
d us
e
Pre-
scrib
ed
Kno
wle
dge
Teacher agency Student agency
Repetition Abstraction Acquisition
Authenticity Agency Identity
Teaching as learning design
The Knowledge-Agency Window
Learning analytics for this
Ruth Deakin Crick Univ Bristol Centre for Systems Learning amp Leadership ldquoPedagogy of Hoperdquo httplearningemergencenet20120921pedagogy-of-hope
analytics grounded in the principles of good
assessment for learning
(not summative assessment for
grading pupils teachers institutions or nations)
27
Assessment for Learning
28
httpassessment-reform-grouporg Few learning analytics are
currently able to take o board the richness of this
original conception of assessment for learning
Assessment for Learning
29
httpassessment-reform-grouporg
Assessment for Learning
30
httpassessment-reform-grouporg
To what extent could automated
feedback be designed and evaluated with
emotional impact in mind
Assessment for Learning
31
httpassessment-reform-grouporg
Can analytics identify proxies
for such advanced qualities
Assessment for Learning
32
httpassessment-reform-grouporg
How do we provide analytics feedback
that does not disempower and de-motivate struggling
learners
analytics for learning conversations
33
Socio-cultural discourse analysis (Mercer et al OU)
bull Disputational talk characterised by disagreement and individualised decision making
bull Cumulative talk in which speakers build positively but uncritically on what the others have said
bull Exploratory talk in which partners engage critically but constructively with each others ideas
34 Mercer N (2004) Sociocultural discourse analysis analysing classroom talk as a social mode of thinking Journal of Applied Linguistics 1(2) 137-168
bull Exploratory talk in which partners engage critically but constructively with each others ideas
bull Statements and suggestions are offered for joint consideration
bull These may be challenged and counter-challenged but challenges are justified and alternative hypotheses are offered
bull Partners all actively participate and opinions are sought and considered before decisions are jointly made
bull Compared with the other two types in Exploratory talk knowledge is made more publicly accountable and reasoning is more visible in the talk
35
Socio-cultural discourse analysis (Mercer et al OU)
Mercer N (2004) Sociocultural discourse analysis analysing classroom talk as a social mode of thinking Journal of Applied Linguistics 1(2) 137-168
Analytics for identifying Exploratory talk
36
Elluminate sessions can be very long ndash lasting for hours or even covering days of a conference
It would be useful if we could identify where quality learning conversations seem to be taking place so we can recommend those sessions and not have to sit through online chat about virtual biscuits
Ferguson R and Buckingham Shum S Learning analytics to identify exploratory dialogue within synchronous text chat 1st International Conference on Learning Analytics amp Knowledge (Banff Canada 27 Mar-1 Apr 2011)
Defining indicators of Exploratory Talk
37
Category Indicator Challenge But if have to respond my view Critique However Irsquom not sure maybe Discussion of resources
Have you read more links
Evaluation Good example good point Explanation Means that our goals Explicit reasoning Next step relates to thatrsquos why Justification I mean we learned we observed Reflections of perspectives of others
Agree here is another makes the point take your point your view
Extract classified as Exploratory Talk
38
Time Contribution 242 PM I hate talking -P My question was whether gadgets were just
basically widgets and we could embed them in various web sites like Netvibes Google Desktop etc
242 PM Thanks thats great I am sure I understood everything but looks inspiring
243 PM Yes why OU tools not generic tools
243 PM Issues of interoperability
243 PM The new SocialLearn site looks a lot like a corkboard where you can add various widgets similar to those existing web start pages
243 PM What if we end up with as many appsgadgets as we have social networks and then we need a recommender for the apps
243 PM My question was on the definition of the crowd in the wisdom of crowds we acsess in the service model
243 PM there are various different flavours of widget eg Google gadgets W3C widgets etc SocialLearn has gone for Google gadgets
-60
-40
-20
0
20
40
60
80
92
8 9
32
93
6
94
0
94
1
94
6
95
0
95
3
95
6
10
00
10
05
10
07
10
07
10
09
10
13
10
17
10
23
10
27
10
31
10
35
10
40
10
45
10
52
10
55
11
04
11
08
11
11
11
17
11
20
11
24
11
26
11
28
11
31
11
32
11
35
11
36
11
38
11
39
11
41
11
44
11
46
11
48
11
52
11
54
12
00
12
03
12
04
12
05
Average Exploratory
Discourse analytics on webinar textchat
Sheffield UK not as sunny as yesterday - still warm Greetings from Hong Kong Morning from Wiltshire sunny here
See you bye for now bye and thank you Bye all for now
Extensions to Ferguson R and Buckingham Shum S (2011) Learning Analytics to Identify Exploratory Dialogue within Synchronous Text Chat Proc 1st Int Conf Learning Analytics amp Knowledge Feb 27-Mar 1 2011 Banff ACM Press Eprint httporoopenacuk28955
Given a 25 hour webinar where in the live textchat were the most effective learning conversations Not at the start and end of a webinar but if we zoom in on a peakhellip
Discourse analytics on webinar textchat
-100
-50
0
50
100
92
8 9
40
9
50
1
000
1
007
1
017
1
031
1
045
1
104
1
117
1
126
1
132
1
138
1
144
1
152
1
203
Averag
Wei amp He extensions to Ferguson R and Buckingham Shum S (2011) Learning Analytics to Identify Exploratory Dialogue within Synchronous Text Chat Proc 1st Int Conf Learning Analytics amp Knowledge Feb 27-Mar 1 2011 Banff ACM Press Eprint httporoopenacuk28955
Classified as ldquoexploratory
talkrdquo
(more substantive for learning)
ldquonon-exploratoryrdquo
Given a 25 hour webinar where in the live textchat were the most effective learning conversations
De Liddo A Buckingham Shum S Quinto I Bachler M and Cannavacciuolo L Discourse-centric learning analytics 1st International Conference on Learning Analytics amp Knowledge (Banff 27 Mar-1 Apr 2011) httporoopenacuk25829
KMirsquos Cohere a web deliberation platform enabling semantic social network and discourse network analytics
Rebecca is playing the role of broker
connecting 2 peersrsquo contributions in meaningful ways
analytics for scholarly writing
42
Discourse analysis (Xerox Incremental Parser)
BACKGROUND KNOWLEDGE
Recent studies indicate hellip
hellip the previously proposed hellip
hellip is universally accepted
NOVELTY
new insights provide direct evidence
we suggest a new approach
results define a novel role
OPEN QUESTION hellip little is known hellip hellip role hellip has been elusive
Current data is insufficient hellip
GENERALIZING emerging as a promising approach Our understanding has grown exponentially growing recognition of the
importance
CONRASTING IDEAS hellip unorthodox view resolves hellip paradoxes hellip
In contrast with previous hypotheses
inconsistent with past findings
SIGNIFICANCE studies have provided important advances
Knowledge is crucial for understanding
valuable information from studies
SURPRISE We have recently observed surprisingly
We have identified unusual The recent discovery suggests intriguing roles
SUMMARIZING The goal of this study Here we show
Altogether our results indicate
Detection of salient sentences in scholarly reports based on the rhetorical signals authors use
Aacutegnes Saacutendor amp OLnet Project httpolnetorgnode512
De Liddo A Saacutendor Aacute and Buckingham Shum S Contested Collective Intelligence Rationale Technologies and a Human-Machine Annotation Study Computer Supported Cooperative Work 21 4-5 (2012) 417-448 httporoopenacuk31052
Human and machine analysis of a text for key contributions
19 sentences annotated 22 sentences annotated 11 sentences same as human annotation
71 sentences annotated 59 sentences annotated 42 sentences same as human annotation
Document 1
Document 2
httptechnologieskmiopenacukcohere20120109cohere-plus-automated-rhetorical-annotation De Liddo A Saacutendor Aacute and Buckingham Shum S Contested Collective Intelligence Rationale Technologies and a Human-Machine Annotation Study Computer Supported Cooperative Work 21 4-5 (2012) 417-448 httporoopenacuk31052
analytics for intepersonal networking
45
Semantic Social Network Analytics
De Liddo A Buckingham Shum S Quinto I Bachler M and Cannavacciuolo L Discourse-centric learning analytics 1st International Conference on Learning Analytics amp Knowledge (Banff 27 Mar-1 Apr 2011) httporoopenacuk25829
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Closing thoughts
54
55
ldquoThe basic question is not what can we measure
The basic question is
what does a good education look likerdquo
(Gardner Campbell)
httpchroniclecomblogstechtherapy20120502episode-95-learning-analytics-could-lead-to-wal-martification-of-college httplak12wikispacescomRecordings
56
Our analytics promote values pedagogy and assessment regimes
Are we clear which master
our analytics serve Are we happy to be judged by them
57
Will learning analytics merely turbocharge the current educational paradigm
mdash which is so often declared
not fit for purposehellip
58
hellipor will learning analytics reflect what we now know about designing authentic
engaged learning developing the new qualities that a
complex society demands
For examples of each level of analytichellip
7 Buckingham Shum S 2012 Our Learning Analytics are Our Pedagogy Keynote Address Expanding Horizons 2012 Conference Macquarie University Sydney httpwwwslidesharenetsbsour-learning-analytics-are-our-pedagogy
The VLEmdashBImdashITS convergence
8
Micro individual user actions
(and hence cohort)
As data migrates up it enriches higher layers normally accustomed to sparse data
Meso institution-wide
Macro regionstatenationalinternational
Aggregation of user traces enriches meso + macro analytics with finer-grained process data
Micro individual user actions
(and hence cohort)
hellipwhich in turn could enrich lower layers mdash local patterns can be cross-validated
Meso institution-wide
Macro regionstatenationalinternational
Aggregation of user traces enriches meso + macro analytics with finer-grained process data
Breadth + depth from macro + meso levels could add power to micro-analytics
anatomy of an analytics ecosystem
11
A learning analytics ecosystem
12
learners
educators
A learning analytics ecosystem
13
learners
educators
A learning analytics ecosystem
14
learners
educators
A learning analytics ecosystem
15
learners
educators
data curators translators
dashboard design team
data capture design team
Where did the data come from
16
learners
Where did the data come from
17
learners
researchers educators instructional designers
theories pedagogies
assessments tools
Where did the data come from
18
learners
researchers educators instructional designers
theories pedagogies
assessments tools
technologists
Data Intent
The map is not the territory Analytics are not the end but a means The goal is to optimize the whole system
19
learners
researchers educators instructional designers
theories pedagogies
assessments tools
desi
gn feedback
intent
outcome
Optimize the system for what
20
Same outcomes but higher scores
Learning Analytics as
Evolutionary Technology
bull more engaging bull better assessed bull better outcomes
bull deliverable at scale
21
New outcomes we couldnrsquot assess before
Learning Analytics as
Revolutionary Technology
bull learner behaviours quantifiable bull interpersonal networks quantifiable
bull discourse quantifiable bull moods and dispositions quantifiable
22
ldquoWe are preparing students for jobs that do not exist yet that will use technologies that have not been invented yet in order to solve problems that are not even problems yetrdquo
ldquoShift Happensrdquo httpshifthappenswikispacescom
23
Learning analytics for this
Learning analytics for this
ldquoWhile employers continue to demand high academic standards they also now want more They want people who can adapt see connections innovate communicate and work with others This is true in many areas of work The new knowledge-based economies in particular will increasingly depend on these abilities Many businesses are paying for courses to promote creative abilities to teach the skills and attitudes that are now essential for economic successhelliprdquo
All our Futures Creativity culture amp education May 1999 24
Learning analytics for this
25
Creativity Culture and Education (2009) Changing Young Lives 2012 Newcastle CCE httpwwwcreativitycultureeducationorgchanging-young-lives-2012
Think about the analytics products and initiatives reviewed above ndash where would you locate them on these dimensions
Expert-led enquiry
Student-led enquiry
Expert-led teaching
Student-led revision
Kno
wle
dge
co
-gen
erat
ion
an
d us
e
Pre-
scrib
ed
Kno
wle
dge
Teacher agency Student agency
Repetition Abstraction Acquisition
Authenticity Agency Identity
Teaching as learning design
The Knowledge-Agency Window
Learning analytics for this
Ruth Deakin Crick Univ Bristol Centre for Systems Learning amp Leadership ldquoPedagogy of Hoperdquo httplearningemergencenet20120921pedagogy-of-hope
analytics grounded in the principles of good
assessment for learning
(not summative assessment for
grading pupils teachers institutions or nations)
27
Assessment for Learning
28
httpassessment-reform-grouporg Few learning analytics are
currently able to take o board the richness of this
original conception of assessment for learning
Assessment for Learning
29
httpassessment-reform-grouporg
Assessment for Learning
30
httpassessment-reform-grouporg
To what extent could automated
feedback be designed and evaluated with
emotional impact in mind
Assessment for Learning
31
httpassessment-reform-grouporg
Can analytics identify proxies
for such advanced qualities
Assessment for Learning
32
httpassessment-reform-grouporg
How do we provide analytics feedback
that does not disempower and de-motivate struggling
learners
analytics for learning conversations
33
Socio-cultural discourse analysis (Mercer et al OU)
bull Disputational talk characterised by disagreement and individualised decision making
bull Cumulative talk in which speakers build positively but uncritically on what the others have said
bull Exploratory talk in which partners engage critically but constructively with each others ideas
34 Mercer N (2004) Sociocultural discourse analysis analysing classroom talk as a social mode of thinking Journal of Applied Linguistics 1(2) 137-168
bull Exploratory talk in which partners engage critically but constructively with each others ideas
bull Statements and suggestions are offered for joint consideration
bull These may be challenged and counter-challenged but challenges are justified and alternative hypotheses are offered
bull Partners all actively participate and opinions are sought and considered before decisions are jointly made
bull Compared with the other two types in Exploratory talk knowledge is made more publicly accountable and reasoning is more visible in the talk
35
Socio-cultural discourse analysis (Mercer et al OU)
Mercer N (2004) Sociocultural discourse analysis analysing classroom talk as a social mode of thinking Journal of Applied Linguistics 1(2) 137-168
Analytics for identifying Exploratory talk
36
Elluminate sessions can be very long ndash lasting for hours or even covering days of a conference
It would be useful if we could identify where quality learning conversations seem to be taking place so we can recommend those sessions and not have to sit through online chat about virtual biscuits
Ferguson R and Buckingham Shum S Learning analytics to identify exploratory dialogue within synchronous text chat 1st International Conference on Learning Analytics amp Knowledge (Banff Canada 27 Mar-1 Apr 2011)
Defining indicators of Exploratory Talk
37
Category Indicator Challenge But if have to respond my view Critique However Irsquom not sure maybe Discussion of resources
Have you read more links
Evaluation Good example good point Explanation Means that our goals Explicit reasoning Next step relates to thatrsquos why Justification I mean we learned we observed Reflections of perspectives of others
Agree here is another makes the point take your point your view
Extract classified as Exploratory Talk
38
Time Contribution 242 PM I hate talking -P My question was whether gadgets were just
basically widgets and we could embed them in various web sites like Netvibes Google Desktop etc
242 PM Thanks thats great I am sure I understood everything but looks inspiring
243 PM Yes why OU tools not generic tools
243 PM Issues of interoperability
243 PM The new SocialLearn site looks a lot like a corkboard where you can add various widgets similar to those existing web start pages
243 PM What if we end up with as many appsgadgets as we have social networks and then we need a recommender for the apps
243 PM My question was on the definition of the crowd in the wisdom of crowds we acsess in the service model
243 PM there are various different flavours of widget eg Google gadgets W3C widgets etc SocialLearn has gone for Google gadgets
-60
-40
-20
0
20
40
60
80
92
8 9
32
93
6
94
0
94
1
94
6
95
0
95
3
95
6
10
00
10
05
10
07
10
07
10
09
10
13
10
17
10
23
10
27
10
31
10
35
10
40
10
45
10
52
10
55
11
04
11
08
11
11
11
17
11
20
11
24
11
26
11
28
11
31
11
32
11
35
11
36
11
38
11
39
11
41
11
44
11
46
11
48
11
52
11
54
12
00
12
03
12
04
12
05
Average Exploratory
Discourse analytics on webinar textchat
Sheffield UK not as sunny as yesterday - still warm Greetings from Hong Kong Morning from Wiltshire sunny here
See you bye for now bye and thank you Bye all for now
Extensions to Ferguson R and Buckingham Shum S (2011) Learning Analytics to Identify Exploratory Dialogue within Synchronous Text Chat Proc 1st Int Conf Learning Analytics amp Knowledge Feb 27-Mar 1 2011 Banff ACM Press Eprint httporoopenacuk28955
Given a 25 hour webinar where in the live textchat were the most effective learning conversations Not at the start and end of a webinar but if we zoom in on a peakhellip
Discourse analytics on webinar textchat
-100
-50
0
50
100
92
8 9
40
9
50
1
000
1
007
1
017
1
031
1
045
1
104
1
117
1
126
1
132
1
138
1
144
1
152
1
203
Averag
Wei amp He extensions to Ferguson R and Buckingham Shum S (2011) Learning Analytics to Identify Exploratory Dialogue within Synchronous Text Chat Proc 1st Int Conf Learning Analytics amp Knowledge Feb 27-Mar 1 2011 Banff ACM Press Eprint httporoopenacuk28955
Classified as ldquoexploratory
talkrdquo
(more substantive for learning)
ldquonon-exploratoryrdquo
Given a 25 hour webinar where in the live textchat were the most effective learning conversations
De Liddo A Buckingham Shum S Quinto I Bachler M and Cannavacciuolo L Discourse-centric learning analytics 1st International Conference on Learning Analytics amp Knowledge (Banff 27 Mar-1 Apr 2011) httporoopenacuk25829
KMirsquos Cohere a web deliberation platform enabling semantic social network and discourse network analytics
Rebecca is playing the role of broker
connecting 2 peersrsquo contributions in meaningful ways
analytics for scholarly writing
42
Discourse analysis (Xerox Incremental Parser)
BACKGROUND KNOWLEDGE
Recent studies indicate hellip
hellip the previously proposed hellip
hellip is universally accepted
NOVELTY
new insights provide direct evidence
we suggest a new approach
results define a novel role
OPEN QUESTION hellip little is known hellip hellip role hellip has been elusive
Current data is insufficient hellip
GENERALIZING emerging as a promising approach Our understanding has grown exponentially growing recognition of the
importance
CONRASTING IDEAS hellip unorthodox view resolves hellip paradoxes hellip
In contrast with previous hypotheses
inconsistent with past findings
SIGNIFICANCE studies have provided important advances
Knowledge is crucial for understanding
valuable information from studies
SURPRISE We have recently observed surprisingly
We have identified unusual The recent discovery suggests intriguing roles
SUMMARIZING The goal of this study Here we show
Altogether our results indicate
Detection of salient sentences in scholarly reports based on the rhetorical signals authors use
Aacutegnes Saacutendor amp OLnet Project httpolnetorgnode512
De Liddo A Saacutendor Aacute and Buckingham Shum S Contested Collective Intelligence Rationale Technologies and a Human-Machine Annotation Study Computer Supported Cooperative Work 21 4-5 (2012) 417-448 httporoopenacuk31052
Human and machine analysis of a text for key contributions
19 sentences annotated 22 sentences annotated 11 sentences same as human annotation
71 sentences annotated 59 sentences annotated 42 sentences same as human annotation
Document 1
Document 2
httptechnologieskmiopenacukcohere20120109cohere-plus-automated-rhetorical-annotation De Liddo A Saacutendor Aacute and Buckingham Shum S Contested Collective Intelligence Rationale Technologies and a Human-Machine Annotation Study Computer Supported Cooperative Work 21 4-5 (2012) 417-448 httporoopenacuk31052
analytics for intepersonal networking
45
Semantic Social Network Analytics
De Liddo A Buckingham Shum S Quinto I Bachler M and Cannavacciuolo L Discourse-centric learning analytics 1st International Conference on Learning Analytics amp Knowledge (Banff 27 Mar-1 Apr 2011) httporoopenacuk25829
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Closing thoughts
54
55
ldquoThe basic question is not what can we measure
The basic question is
what does a good education look likerdquo
(Gardner Campbell)
httpchroniclecomblogstechtherapy20120502episode-95-learning-analytics-could-lead-to-wal-martification-of-college httplak12wikispacescomRecordings
56
Our analytics promote values pedagogy and assessment regimes
Are we clear which master
our analytics serve Are we happy to be judged by them
57
Will learning analytics merely turbocharge the current educational paradigm
mdash which is so often declared
not fit for purposehellip
58
hellipor will learning analytics reflect what we now know about designing authentic
engaged learning developing the new qualities that a
complex society demands
The VLEmdashBImdashITS convergence
8
Micro individual user actions
(and hence cohort)
As data migrates up it enriches higher layers normally accustomed to sparse data
Meso institution-wide
Macro regionstatenationalinternational
Aggregation of user traces enriches meso + macro analytics with finer-grained process data
Micro individual user actions
(and hence cohort)
hellipwhich in turn could enrich lower layers mdash local patterns can be cross-validated
Meso institution-wide
Macro regionstatenationalinternational
Aggregation of user traces enriches meso + macro analytics with finer-grained process data
Breadth + depth from macro + meso levels could add power to micro-analytics
anatomy of an analytics ecosystem
11
A learning analytics ecosystem
12
learners
educators
A learning analytics ecosystem
13
learners
educators
A learning analytics ecosystem
14
learners
educators
A learning analytics ecosystem
15
learners
educators
data curators translators
dashboard design team
data capture design team
Where did the data come from
16
learners
Where did the data come from
17
learners
researchers educators instructional designers
theories pedagogies
assessments tools
Where did the data come from
18
learners
researchers educators instructional designers
theories pedagogies
assessments tools
technologists
Data Intent
The map is not the territory Analytics are not the end but a means The goal is to optimize the whole system
19
learners
researchers educators instructional designers
theories pedagogies
assessments tools
desi
gn feedback
intent
outcome
Optimize the system for what
20
Same outcomes but higher scores
Learning Analytics as
Evolutionary Technology
bull more engaging bull better assessed bull better outcomes
bull deliverable at scale
21
New outcomes we couldnrsquot assess before
Learning Analytics as
Revolutionary Technology
bull learner behaviours quantifiable bull interpersonal networks quantifiable
bull discourse quantifiable bull moods and dispositions quantifiable
22
ldquoWe are preparing students for jobs that do not exist yet that will use technologies that have not been invented yet in order to solve problems that are not even problems yetrdquo
ldquoShift Happensrdquo httpshifthappenswikispacescom
23
Learning analytics for this
Learning analytics for this
ldquoWhile employers continue to demand high academic standards they also now want more They want people who can adapt see connections innovate communicate and work with others This is true in many areas of work The new knowledge-based economies in particular will increasingly depend on these abilities Many businesses are paying for courses to promote creative abilities to teach the skills and attitudes that are now essential for economic successhelliprdquo
All our Futures Creativity culture amp education May 1999 24
Learning analytics for this
25
Creativity Culture and Education (2009) Changing Young Lives 2012 Newcastle CCE httpwwwcreativitycultureeducationorgchanging-young-lives-2012
Think about the analytics products and initiatives reviewed above ndash where would you locate them on these dimensions
Expert-led enquiry
Student-led enquiry
Expert-led teaching
Student-led revision
Kno
wle
dge
co
-gen
erat
ion
an
d us
e
Pre-
scrib
ed
Kno
wle
dge
Teacher agency Student agency
Repetition Abstraction Acquisition
Authenticity Agency Identity
Teaching as learning design
The Knowledge-Agency Window
Learning analytics for this
Ruth Deakin Crick Univ Bristol Centre for Systems Learning amp Leadership ldquoPedagogy of Hoperdquo httplearningemergencenet20120921pedagogy-of-hope
analytics grounded in the principles of good
assessment for learning
(not summative assessment for
grading pupils teachers institutions or nations)
27
Assessment for Learning
28
httpassessment-reform-grouporg Few learning analytics are
currently able to take o board the richness of this
original conception of assessment for learning
Assessment for Learning
29
httpassessment-reform-grouporg
Assessment for Learning
30
httpassessment-reform-grouporg
To what extent could automated
feedback be designed and evaluated with
emotional impact in mind
Assessment for Learning
31
httpassessment-reform-grouporg
Can analytics identify proxies
for such advanced qualities
Assessment for Learning
32
httpassessment-reform-grouporg
How do we provide analytics feedback
that does not disempower and de-motivate struggling
learners
analytics for learning conversations
33
Socio-cultural discourse analysis (Mercer et al OU)
bull Disputational talk characterised by disagreement and individualised decision making
bull Cumulative talk in which speakers build positively but uncritically on what the others have said
bull Exploratory talk in which partners engage critically but constructively with each others ideas
34 Mercer N (2004) Sociocultural discourse analysis analysing classroom talk as a social mode of thinking Journal of Applied Linguistics 1(2) 137-168
bull Exploratory talk in which partners engage critically but constructively with each others ideas
bull Statements and suggestions are offered for joint consideration
bull These may be challenged and counter-challenged but challenges are justified and alternative hypotheses are offered
bull Partners all actively participate and opinions are sought and considered before decisions are jointly made
bull Compared with the other two types in Exploratory talk knowledge is made more publicly accountable and reasoning is more visible in the talk
35
Socio-cultural discourse analysis (Mercer et al OU)
Mercer N (2004) Sociocultural discourse analysis analysing classroom talk as a social mode of thinking Journal of Applied Linguistics 1(2) 137-168
Analytics for identifying Exploratory talk
36
Elluminate sessions can be very long ndash lasting for hours or even covering days of a conference
It would be useful if we could identify where quality learning conversations seem to be taking place so we can recommend those sessions and not have to sit through online chat about virtual biscuits
Ferguson R and Buckingham Shum S Learning analytics to identify exploratory dialogue within synchronous text chat 1st International Conference on Learning Analytics amp Knowledge (Banff Canada 27 Mar-1 Apr 2011)
Defining indicators of Exploratory Talk
37
Category Indicator Challenge But if have to respond my view Critique However Irsquom not sure maybe Discussion of resources
Have you read more links
Evaluation Good example good point Explanation Means that our goals Explicit reasoning Next step relates to thatrsquos why Justification I mean we learned we observed Reflections of perspectives of others
Agree here is another makes the point take your point your view
Extract classified as Exploratory Talk
38
Time Contribution 242 PM I hate talking -P My question was whether gadgets were just
basically widgets and we could embed them in various web sites like Netvibes Google Desktop etc
242 PM Thanks thats great I am sure I understood everything but looks inspiring
243 PM Yes why OU tools not generic tools
243 PM Issues of interoperability
243 PM The new SocialLearn site looks a lot like a corkboard where you can add various widgets similar to those existing web start pages
243 PM What if we end up with as many appsgadgets as we have social networks and then we need a recommender for the apps
243 PM My question was on the definition of the crowd in the wisdom of crowds we acsess in the service model
243 PM there are various different flavours of widget eg Google gadgets W3C widgets etc SocialLearn has gone for Google gadgets
-60
-40
-20
0
20
40
60
80
92
8 9
32
93
6
94
0
94
1
94
6
95
0
95
3
95
6
10
00
10
05
10
07
10
07
10
09
10
13
10
17
10
23
10
27
10
31
10
35
10
40
10
45
10
52
10
55
11
04
11
08
11
11
11
17
11
20
11
24
11
26
11
28
11
31
11
32
11
35
11
36
11
38
11
39
11
41
11
44
11
46
11
48
11
52
11
54
12
00
12
03
12
04
12
05
Average Exploratory
Discourse analytics on webinar textchat
Sheffield UK not as sunny as yesterday - still warm Greetings from Hong Kong Morning from Wiltshire sunny here
See you bye for now bye and thank you Bye all for now
Extensions to Ferguson R and Buckingham Shum S (2011) Learning Analytics to Identify Exploratory Dialogue within Synchronous Text Chat Proc 1st Int Conf Learning Analytics amp Knowledge Feb 27-Mar 1 2011 Banff ACM Press Eprint httporoopenacuk28955
Given a 25 hour webinar where in the live textchat were the most effective learning conversations Not at the start and end of a webinar but if we zoom in on a peakhellip
Discourse analytics on webinar textchat
-100
-50
0
50
100
92
8 9
40
9
50
1
000
1
007
1
017
1
031
1
045
1
104
1
117
1
126
1
132
1
138
1
144
1
152
1
203
Averag
Wei amp He extensions to Ferguson R and Buckingham Shum S (2011) Learning Analytics to Identify Exploratory Dialogue within Synchronous Text Chat Proc 1st Int Conf Learning Analytics amp Knowledge Feb 27-Mar 1 2011 Banff ACM Press Eprint httporoopenacuk28955
Classified as ldquoexploratory
talkrdquo
(more substantive for learning)
ldquonon-exploratoryrdquo
Given a 25 hour webinar where in the live textchat were the most effective learning conversations
De Liddo A Buckingham Shum S Quinto I Bachler M and Cannavacciuolo L Discourse-centric learning analytics 1st International Conference on Learning Analytics amp Knowledge (Banff 27 Mar-1 Apr 2011) httporoopenacuk25829
KMirsquos Cohere a web deliberation platform enabling semantic social network and discourse network analytics
Rebecca is playing the role of broker
connecting 2 peersrsquo contributions in meaningful ways
analytics for scholarly writing
42
Discourse analysis (Xerox Incremental Parser)
BACKGROUND KNOWLEDGE
Recent studies indicate hellip
hellip the previously proposed hellip
hellip is universally accepted
NOVELTY
new insights provide direct evidence
we suggest a new approach
results define a novel role
OPEN QUESTION hellip little is known hellip hellip role hellip has been elusive
Current data is insufficient hellip
GENERALIZING emerging as a promising approach Our understanding has grown exponentially growing recognition of the
importance
CONRASTING IDEAS hellip unorthodox view resolves hellip paradoxes hellip
In contrast with previous hypotheses
inconsistent with past findings
SIGNIFICANCE studies have provided important advances
Knowledge is crucial for understanding
valuable information from studies
SURPRISE We have recently observed surprisingly
We have identified unusual The recent discovery suggests intriguing roles
SUMMARIZING The goal of this study Here we show
Altogether our results indicate
Detection of salient sentences in scholarly reports based on the rhetorical signals authors use
Aacutegnes Saacutendor amp OLnet Project httpolnetorgnode512
De Liddo A Saacutendor Aacute and Buckingham Shum S Contested Collective Intelligence Rationale Technologies and a Human-Machine Annotation Study Computer Supported Cooperative Work 21 4-5 (2012) 417-448 httporoopenacuk31052
Human and machine analysis of a text for key contributions
19 sentences annotated 22 sentences annotated 11 sentences same as human annotation
71 sentences annotated 59 sentences annotated 42 sentences same as human annotation
Document 1
Document 2
httptechnologieskmiopenacukcohere20120109cohere-plus-automated-rhetorical-annotation De Liddo A Saacutendor Aacute and Buckingham Shum S Contested Collective Intelligence Rationale Technologies and a Human-Machine Annotation Study Computer Supported Cooperative Work 21 4-5 (2012) 417-448 httporoopenacuk31052
analytics for intepersonal networking
45
Semantic Social Network Analytics
De Liddo A Buckingham Shum S Quinto I Bachler M and Cannavacciuolo L Discourse-centric learning analytics 1st International Conference on Learning Analytics amp Knowledge (Banff 27 Mar-1 Apr 2011) httporoopenacuk25829
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Closing thoughts
54
55
ldquoThe basic question is not what can we measure
The basic question is
what does a good education look likerdquo
(Gardner Campbell)
httpchroniclecomblogstechtherapy20120502episode-95-learning-analytics-could-lead-to-wal-martification-of-college httplak12wikispacescomRecordings
56
Our analytics promote values pedagogy and assessment regimes
Are we clear which master
our analytics serve Are we happy to be judged by them
57
Will learning analytics merely turbocharge the current educational paradigm
mdash which is so often declared
not fit for purposehellip
58
hellipor will learning analytics reflect what we now know about designing authentic
engaged learning developing the new qualities that a
complex society demands
Micro individual user actions
(and hence cohort)
As data migrates up it enriches higher layers normally accustomed to sparse data
Meso institution-wide
Macro regionstatenationalinternational
Aggregation of user traces enriches meso + macro analytics with finer-grained process data
Micro individual user actions
(and hence cohort)
hellipwhich in turn could enrich lower layers mdash local patterns can be cross-validated
Meso institution-wide
Macro regionstatenationalinternational
Aggregation of user traces enriches meso + macro analytics with finer-grained process data
Breadth + depth from macro + meso levels could add power to micro-analytics
anatomy of an analytics ecosystem
11
A learning analytics ecosystem
12
learners
educators
A learning analytics ecosystem
13
learners
educators
A learning analytics ecosystem
14
learners
educators
A learning analytics ecosystem
15
learners
educators
data curators translators
dashboard design team
data capture design team
Where did the data come from
16
learners
Where did the data come from
17
learners
researchers educators instructional designers
theories pedagogies
assessments tools
Where did the data come from
18
learners
researchers educators instructional designers
theories pedagogies
assessments tools
technologists
Data Intent
The map is not the territory Analytics are not the end but a means The goal is to optimize the whole system
19
learners
researchers educators instructional designers
theories pedagogies
assessments tools
desi
gn feedback
intent
outcome
Optimize the system for what
20
Same outcomes but higher scores
Learning Analytics as
Evolutionary Technology
bull more engaging bull better assessed bull better outcomes
bull deliverable at scale
21
New outcomes we couldnrsquot assess before
Learning Analytics as
Revolutionary Technology
bull learner behaviours quantifiable bull interpersonal networks quantifiable
bull discourse quantifiable bull moods and dispositions quantifiable
22
ldquoWe are preparing students for jobs that do not exist yet that will use technologies that have not been invented yet in order to solve problems that are not even problems yetrdquo
ldquoShift Happensrdquo httpshifthappenswikispacescom
23
Learning analytics for this
Learning analytics for this
ldquoWhile employers continue to demand high academic standards they also now want more They want people who can adapt see connections innovate communicate and work with others This is true in many areas of work The new knowledge-based economies in particular will increasingly depend on these abilities Many businesses are paying for courses to promote creative abilities to teach the skills and attitudes that are now essential for economic successhelliprdquo
All our Futures Creativity culture amp education May 1999 24
Learning analytics for this
25
Creativity Culture and Education (2009) Changing Young Lives 2012 Newcastle CCE httpwwwcreativitycultureeducationorgchanging-young-lives-2012
Think about the analytics products and initiatives reviewed above ndash where would you locate them on these dimensions
Expert-led enquiry
Student-led enquiry
Expert-led teaching
Student-led revision
Kno
wle
dge
co
-gen
erat
ion
an
d us
e
Pre-
scrib
ed
Kno
wle
dge
Teacher agency Student agency
Repetition Abstraction Acquisition
Authenticity Agency Identity
Teaching as learning design
The Knowledge-Agency Window
Learning analytics for this
Ruth Deakin Crick Univ Bristol Centre for Systems Learning amp Leadership ldquoPedagogy of Hoperdquo httplearningemergencenet20120921pedagogy-of-hope
analytics grounded in the principles of good
assessment for learning
(not summative assessment for
grading pupils teachers institutions or nations)
27
Assessment for Learning
28
httpassessment-reform-grouporg Few learning analytics are
currently able to take o board the richness of this
original conception of assessment for learning
Assessment for Learning
29
httpassessment-reform-grouporg
Assessment for Learning
30
httpassessment-reform-grouporg
To what extent could automated
feedback be designed and evaluated with
emotional impact in mind
Assessment for Learning
31
httpassessment-reform-grouporg
Can analytics identify proxies
for such advanced qualities
Assessment for Learning
32
httpassessment-reform-grouporg
How do we provide analytics feedback
that does not disempower and de-motivate struggling
learners
analytics for learning conversations
33
Socio-cultural discourse analysis (Mercer et al OU)
bull Disputational talk characterised by disagreement and individualised decision making
bull Cumulative talk in which speakers build positively but uncritically on what the others have said
bull Exploratory talk in which partners engage critically but constructively with each others ideas
34 Mercer N (2004) Sociocultural discourse analysis analysing classroom talk as a social mode of thinking Journal of Applied Linguistics 1(2) 137-168
bull Exploratory talk in which partners engage critically but constructively with each others ideas
bull Statements and suggestions are offered for joint consideration
bull These may be challenged and counter-challenged but challenges are justified and alternative hypotheses are offered
bull Partners all actively participate and opinions are sought and considered before decisions are jointly made
bull Compared with the other two types in Exploratory talk knowledge is made more publicly accountable and reasoning is more visible in the talk
35
Socio-cultural discourse analysis (Mercer et al OU)
Mercer N (2004) Sociocultural discourse analysis analysing classroom talk as a social mode of thinking Journal of Applied Linguistics 1(2) 137-168
Analytics for identifying Exploratory talk
36
Elluminate sessions can be very long ndash lasting for hours or even covering days of a conference
It would be useful if we could identify where quality learning conversations seem to be taking place so we can recommend those sessions and not have to sit through online chat about virtual biscuits
Ferguson R and Buckingham Shum S Learning analytics to identify exploratory dialogue within synchronous text chat 1st International Conference on Learning Analytics amp Knowledge (Banff Canada 27 Mar-1 Apr 2011)
Defining indicators of Exploratory Talk
37
Category Indicator Challenge But if have to respond my view Critique However Irsquom not sure maybe Discussion of resources
Have you read more links
Evaluation Good example good point Explanation Means that our goals Explicit reasoning Next step relates to thatrsquos why Justification I mean we learned we observed Reflections of perspectives of others
Agree here is another makes the point take your point your view
Extract classified as Exploratory Talk
38
Time Contribution 242 PM I hate talking -P My question was whether gadgets were just
basically widgets and we could embed them in various web sites like Netvibes Google Desktop etc
242 PM Thanks thats great I am sure I understood everything but looks inspiring
243 PM Yes why OU tools not generic tools
243 PM Issues of interoperability
243 PM The new SocialLearn site looks a lot like a corkboard where you can add various widgets similar to those existing web start pages
243 PM What if we end up with as many appsgadgets as we have social networks and then we need a recommender for the apps
243 PM My question was on the definition of the crowd in the wisdom of crowds we acsess in the service model
243 PM there are various different flavours of widget eg Google gadgets W3C widgets etc SocialLearn has gone for Google gadgets
-60
-40
-20
0
20
40
60
80
92
8 9
32
93
6
94
0
94
1
94
6
95
0
95
3
95
6
10
00
10
05
10
07
10
07
10
09
10
13
10
17
10
23
10
27
10
31
10
35
10
40
10
45
10
52
10
55
11
04
11
08
11
11
11
17
11
20
11
24
11
26
11
28
11
31
11
32
11
35
11
36
11
38
11
39
11
41
11
44
11
46
11
48
11
52
11
54
12
00
12
03
12
04
12
05
Average Exploratory
Discourse analytics on webinar textchat
Sheffield UK not as sunny as yesterday - still warm Greetings from Hong Kong Morning from Wiltshire sunny here
See you bye for now bye and thank you Bye all for now
Extensions to Ferguson R and Buckingham Shum S (2011) Learning Analytics to Identify Exploratory Dialogue within Synchronous Text Chat Proc 1st Int Conf Learning Analytics amp Knowledge Feb 27-Mar 1 2011 Banff ACM Press Eprint httporoopenacuk28955
Given a 25 hour webinar where in the live textchat were the most effective learning conversations Not at the start and end of a webinar but if we zoom in on a peakhellip
Discourse analytics on webinar textchat
-100
-50
0
50
100
92
8 9
40
9
50
1
000
1
007
1
017
1
031
1
045
1
104
1
117
1
126
1
132
1
138
1
144
1
152
1
203
Averag
Wei amp He extensions to Ferguson R and Buckingham Shum S (2011) Learning Analytics to Identify Exploratory Dialogue within Synchronous Text Chat Proc 1st Int Conf Learning Analytics amp Knowledge Feb 27-Mar 1 2011 Banff ACM Press Eprint httporoopenacuk28955
Classified as ldquoexploratory
talkrdquo
(more substantive for learning)
ldquonon-exploratoryrdquo
Given a 25 hour webinar where in the live textchat were the most effective learning conversations
De Liddo A Buckingham Shum S Quinto I Bachler M and Cannavacciuolo L Discourse-centric learning analytics 1st International Conference on Learning Analytics amp Knowledge (Banff 27 Mar-1 Apr 2011) httporoopenacuk25829
KMirsquos Cohere a web deliberation platform enabling semantic social network and discourse network analytics
Rebecca is playing the role of broker
connecting 2 peersrsquo contributions in meaningful ways
analytics for scholarly writing
42
Discourse analysis (Xerox Incremental Parser)
BACKGROUND KNOWLEDGE
Recent studies indicate hellip
hellip the previously proposed hellip
hellip is universally accepted
NOVELTY
new insights provide direct evidence
we suggest a new approach
results define a novel role
OPEN QUESTION hellip little is known hellip hellip role hellip has been elusive
Current data is insufficient hellip
GENERALIZING emerging as a promising approach Our understanding has grown exponentially growing recognition of the
importance
CONRASTING IDEAS hellip unorthodox view resolves hellip paradoxes hellip
In contrast with previous hypotheses
inconsistent with past findings
SIGNIFICANCE studies have provided important advances
Knowledge is crucial for understanding
valuable information from studies
SURPRISE We have recently observed surprisingly
We have identified unusual The recent discovery suggests intriguing roles
SUMMARIZING The goal of this study Here we show
Altogether our results indicate
Detection of salient sentences in scholarly reports based on the rhetorical signals authors use
Aacutegnes Saacutendor amp OLnet Project httpolnetorgnode512
De Liddo A Saacutendor Aacute and Buckingham Shum S Contested Collective Intelligence Rationale Technologies and a Human-Machine Annotation Study Computer Supported Cooperative Work 21 4-5 (2012) 417-448 httporoopenacuk31052
Human and machine analysis of a text for key contributions
19 sentences annotated 22 sentences annotated 11 sentences same as human annotation
71 sentences annotated 59 sentences annotated 42 sentences same as human annotation
Document 1
Document 2
httptechnologieskmiopenacukcohere20120109cohere-plus-automated-rhetorical-annotation De Liddo A Saacutendor Aacute and Buckingham Shum S Contested Collective Intelligence Rationale Technologies and a Human-Machine Annotation Study Computer Supported Cooperative Work 21 4-5 (2012) 417-448 httporoopenacuk31052
analytics for intepersonal networking
45
Semantic Social Network Analytics
De Liddo A Buckingham Shum S Quinto I Bachler M and Cannavacciuolo L Discourse-centric learning analytics 1st International Conference on Learning Analytics amp Knowledge (Banff 27 Mar-1 Apr 2011) httporoopenacuk25829
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Closing thoughts
54
55
ldquoThe basic question is not what can we measure
The basic question is
what does a good education look likerdquo
(Gardner Campbell)
httpchroniclecomblogstechtherapy20120502episode-95-learning-analytics-could-lead-to-wal-martification-of-college httplak12wikispacescomRecordings
56
Our analytics promote values pedagogy and assessment regimes
Are we clear which master
our analytics serve Are we happy to be judged by them
57
Will learning analytics merely turbocharge the current educational paradigm
mdash which is so often declared
not fit for purposehellip
58
hellipor will learning analytics reflect what we now know about designing authentic
engaged learning developing the new qualities that a
complex society demands
Micro individual user actions
(and hence cohort)
hellipwhich in turn could enrich lower layers mdash local patterns can be cross-validated
Meso institution-wide
Macro regionstatenationalinternational
Aggregation of user traces enriches meso + macro analytics with finer-grained process data
Breadth + depth from macro + meso levels could add power to micro-analytics
anatomy of an analytics ecosystem
11
A learning analytics ecosystem
12
learners
educators
A learning analytics ecosystem
13
learners
educators
A learning analytics ecosystem
14
learners
educators
A learning analytics ecosystem
15
learners
educators
data curators translators
dashboard design team
data capture design team
Where did the data come from
16
learners
Where did the data come from
17
learners
researchers educators instructional designers
theories pedagogies
assessments tools
Where did the data come from
18
learners
researchers educators instructional designers
theories pedagogies
assessments tools
technologists
Data Intent
The map is not the territory Analytics are not the end but a means The goal is to optimize the whole system
19
learners
researchers educators instructional designers
theories pedagogies
assessments tools
desi
gn feedback
intent
outcome
Optimize the system for what
20
Same outcomes but higher scores
Learning Analytics as
Evolutionary Technology
bull more engaging bull better assessed bull better outcomes
bull deliverable at scale
21
New outcomes we couldnrsquot assess before
Learning Analytics as
Revolutionary Technology
bull learner behaviours quantifiable bull interpersonal networks quantifiable
bull discourse quantifiable bull moods and dispositions quantifiable
22
ldquoWe are preparing students for jobs that do not exist yet that will use technologies that have not been invented yet in order to solve problems that are not even problems yetrdquo
ldquoShift Happensrdquo httpshifthappenswikispacescom
23
Learning analytics for this
Learning analytics for this
ldquoWhile employers continue to demand high academic standards they also now want more They want people who can adapt see connections innovate communicate and work with others This is true in many areas of work The new knowledge-based economies in particular will increasingly depend on these abilities Many businesses are paying for courses to promote creative abilities to teach the skills and attitudes that are now essential for economic successhelliprdquo
All our Futures Creativity culture amp education May 1999 24
Learning analytics for this
25
Creativity Culture and Education (2009) Changing Young Lives 2012 Newcastle CCE httpwwwcreativitycultureeducationorgchanging-young-lives-2012
Think about the analytics products and initiatives reviewed above ndash where would you locate them on these dimensions
Expert-led enquiry
Student-led enquiry
Expert-led teaching
Student-led revision
Kno
wle
dge
co
-gen
erat
ion
an
d us
e
Pre-
scrib
ed
Kno
wle
dge
Teacher agency Student agency
Repetition Abstraction Acquisition
Authenticity Agency Identity
Teaching as learning design
The Knowledge-Agency Window
Learning analytics for this
Ruth Deakin Crick Univ Bristol Centre for Systems Learning amp Leadership ldquoPedagogy of Hoperdquo httplearningemergencenet20120921pedagogy-of-hope
analytics grounded in the principles of good
assessment for learning
(not summative assessment for
grading pupils teachers institutions or nations)
27
Assessment for Learning
28
httpassessment-reform-grouporg Few learning analytics are
currently able to take o board the richness of this
original conception of assessment for learning
Assessment for Learning
29
httpassessment-reform-grouporg
Assessment for Learning
30
httpassessment-reform-grouporg
To what extent could automated
feedback be designed and evaluated with
emotional impact in mind
Assessment for Learning
31
httpassessment-reform-grouporg
Can analytics identify proxies
for such advanced qualities
Assessment for Learning
32
httpassessment-reform-grouporg
How do we provide analytics feedback
that does not disempower and de-motivate struggling
learners
analytics for learning conversations
33
Socio-cultural discourse analysis (Mercer et al OU)
bull Disputational talk characterised by disagreement and individualised decision making
bull Cumulative talk in which speakers build positively but uncritically on what the others have said
bull Exploratory talk in which partners engage critically but constructively with each others ideas
34 Mercer N (2004) Sociocultural discourse analysis analysing classroom talk as a social mode of thinking Journal of Applied Linguistics 1(2) 137-168
bull Exploratory talk in which partners engage critically but constructively with each others ideas
bull Statements and suggestions are offered for joint consideration
bull These may be challenged and counter-challenged but challenges are justified and alternative hypotheses are offered
bull Partners all actively participate and opinions are sought and considered before decisions are jointly made
bull Compared with the other two types in Exploratory talk knowledge is made more publicly accountable and reasoning is more visible in the talk
35
Socio-cultural discourse analysis (Mercer et al OU)
Mercer N (2004) Sociocultural discourse analysis analysing classroom talk as a social mode of thinking Journal of Applied Linguistics 1(2) 137-168
Analytics for identifying Exploratory talk
36
Elluminate sessions can be very long ndash lasting for hours or even covering days of a conference
It would be useful if we could identify where quality learning conversations seem to be taking place so we can recommend those sessions and not have to sit through online chat about virtual biscuits
Ferguson R and Buckingham Shum S Learning analytics to identify exploratory dialogue within synchronous text chat 1st International Conference on Learning Analytics amp Knowledge (Banff Canada 27 Mar-1 Apr 2011)
Defining indicators of Exploratory Talk
37
Category Indicator Challenge But if have to respond my view Critique However Irsquom not sure maybe Discussion of resources
Have you read more links
Evaluation Good example good point Explanation Means that our goals Explicit reasoning Next step relates to thatrsquos why Justification I mean we learned we observed Reflections of perspectives of others
Agree here is another makes the point take your point your view
Extract classified as Exploratory Talk
38
Time Contribution 242 PM I hate talking -P My question was whether gadgets were just
basically widgets and we could embed them in various web sites like Netvibes Google Desktop etc
242 PM Thanks thats great I am sure I understood everything but looks inspiring
243 PM Yes why OU tools not generic tools
243 PM Issues of interoperability
243 PM The new SocialLearn site looks a lot like a corkboard where you can add various widgets similar to those existing web start pages
243 PM What if we end up with as many appsgadgets as we have social networks and then we need a recommender for the apps
243 PM My question was on the definition of the crowd in the wisdom of crowds we acsess in the service model
243 PM there are various different flavours of widget eg Google gadgets W3C widgets etc SocialLearn has gone for Google gadgets
-60
-40
-20
0
20
40
60
80
92
8 9
32
93
6
94
0
94
1
94
6
95
0
95
3
95
6
10
00
10
05
10
07
10
07
10
09
10
13
10
17
10
23
10
27
10
31
10
35
10
40
10
45
10
52
10
55
11
04
11
08
11
11
11
17
11
20
11
24
11
26
11
28
11
31
11
32
11
35
11
36
11
38
11
39
11
41
11
44
11
46
11
48
11
52
11
54
12
00
12
03
12
04
12
05
Average Exploratory
Discourse analytics on webinar textchat
Sheffield UK not as sunny as yesterday - still warm Greetings from Hong Kong Morning from Wiltshire sunny here
See you bye for now bye and thank you Bye all for now
Extensions to Ferguson R and Buckingham Shum S (2011) Learning Analytics to Identify Exploratory Dialogue within Synchronous Text Chat Proc 1st Int Conf Learning Analytics amp Knowledge Feb 27-Mar 1 2011 Banff ACM Press Eprint httporoopenacuk28955
Given a 25 hour webinar where in the live textchat were the most effective learning conversations Not at the start and end of a webinar but if we zoom in on a peakhellip
Discourse analytics on webinar textchat
-100
-50
0
50
100
92
8 9
40
9
50
1
000
1
007
1
017
1
031
1
045
1
104
1
117
1
126
1
132
1
138
1
144
1
152
1
203
Averag
Wei amp He extensions to Ferguson R and Buckingham Shum S (2011) Learning Analytics to Identify Exploratory Dialogue within Synchronous Text Chat Proc 1st Int Conf Learning Analytics amp Knowledge Feb 27-Mar 1 2011 Banff ACM Press Eprint httporoopenacuk28955
Classified as ldquoexploratory
talkrdquo
(more substantive for learning)
ldquonon-exploratoryrdquo
Given a 25 hour webinar where in the live textchat were the most effective learning conversations
De Liddo A Buckingham Shum S Quinto I Bachler M and Cannavacciuolo L Discourse-centric learning analytics 1st International Conference on Learning Analytics amp Knowledge (Banff 27 Mar-1 Apr 2011) httporoopenacuk25829
KMirsquos Cohere a web deliberation platform enabling semantic social network and discourse network analytics
Rebecca is playing the role of broker
connecting 2 peersrsquo contributions in meaningful ways
analytics for scholarly writing
42
Discourse analysis (Xerox Incremental Parser)
BACKGROUND KNOWLEDGE
Recent studies indicate hellip
hellip the previously proposed hellip
hellip is universally accepted
NOVELTY
new insights provide direct evidence
we suggest a new approach
results define a novel role
OPEN QUESTION hellip little is known hellip hellip role hellip has been elusive
Current data is insufficient hellip
GENERALIZING emerging as a promising approach Our understanding has grown exponentially growing recognition of the
importance
CONRASTING IDEAS hellip unorthodox view resolves hellip paradoxes hellip
In contrast with previous hypotheses
inconsistent with past findings
SIGNIFICANCE studies have provided important advances
Knowledge is crucial for understanding
valuable information from studies
SURPRISE We have recently observed surprisingly
We have identified unusual The recent discovery suggests intriguing roles
SUMMARIZING The goal of this study Here we show
Altogether our results indicate
Detection of salient sentences in scholarly reports based on the rhetorical signals authors use
Aacutegnes Saacutendor amp OLnet Project httpolnetorgnode512
De Liddo A Saacutendor Aacute and Buckingham Shum S Contested Collective Intelligence Rationale Technologies and a Human-Machine Annotation Study Computer Supported Cooperative Work 21 4-5 (2012) 417-448 httporoopenacuk31052
Human and machine analysis of a text for key contributions
19 sentences annotated 22 sentences annotated 11 sentences same as human annotation
71 sentences annotated 59 sentences annotated 42 sentences same as human annotation
Document 1
Document 2
httptechnologieskmiopenacukcohere20120109cohere-plus-automated-rhetorical-annotation De Liddo A Saacutendor Aacute and Buckingham Shum S Contested Collective Intelligence Rationale Technologies and a Human-Machine Annotation Study Computer Supported Cooperative Work 21 4-5 (2012) 417-448 httporoopenacuk31052
analytics for intepersonal networking
45
Semantic Social Network Analytics
De Liddo A Buckingham Shum S Quinto I Bachler M and Cannavacciuolo L Discourse-centric learning analytics 1st International Conference on Learning Analytics amp Knowledge (Banff 27 Mar-1 Apr 2011) httporoopenacuk25829
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Closing thoughts
54
55
ldquoThe basic question is not what can we measure
The basic question is
what does a good education look likerdquo
(Gardner Campbell)
httpchroniclecomblogstechtherapy20120502episode-95-learning-analytics-could-lead-to-wal-martification-of-college httplak12wikispacescomRecordings
56
Our analytics promote values pedagogy and assessment regimes
Are we clear which master
our analytics serve Are we happy to be judged by them
57
Will learning analytics merely turbocharge the current educational paradigm
mdash which is so often declared
not fit for purposehellip
58
hellipor will learning analytics reflect what we now know about designing authentic
engaged learning developing the new qualities that a
complex society demands
anatomy of an analytics ecosystem
11
A learning analytics ecosystem
12
learners
educators
A learning analytics ecosystem
13
learners
educators
A learning analytics ecosystem
14
learners
educators
A learning analytics ecosystem
15
learners
educators
data curators translators
dashboard design team
data capture design team
Where did the data come from
16
learners
Where did the data come from
17
learners
researchers educators instructional designers
theories pedagogies
assessments tools
Where did the data come from
18
learners
researchers educators instructional designers
theories pedagogies
assessments tools
technologists
Data Intent
The map is not the territory Analytics are not the end but a means The goal is to optimize the whole system
19
learners
researchers educators instructional designers
theories pedagogies
assessments tools
desi
gn feedback
intent
outcome
Optimize the system for what
20
Same outcomes but higher scores
Learning Analytics as
Evolutionary Technology
bull more engaging bull better assessed bull better outcomes
bull deliverable at scale
21
New outcomes we couldnrsquot assess before
Learning Analytics as
Revolutionary Technology
bull learner behaviours quantifiable bull interpersonal networks quantifiable
bull discourse quantifiable bull moods and dispositions quantifiable
22
ldquoWe are preparing students for jobs that do not exist yet that will use technologies that have not been invented yet in order to solve problems that are not even problems yetrdquo
ldquoShift Happensrdquo httpshifthappenswikispacescom
23
Learning analytics for this
Learning analytics for this
ldquoWhile employers continue to demand high academic standards they also now want more They want people who can adapt see connections innovate communicate and work with others This is true in many areas of work The new knowledge-based economies in particular will increasingly depend on these abilities Many businesses are paying for courses to promote creative abilities to teach the skills and attitudes that are now essential for economic successhelliprdquo
All our Futures Creativity culture amp education May 1999 24
Learning analytics for this
25
Creativity Culture and Education (2009) Changing Young Lives 2012 Newcastle CCE httpwwwcreativitycultureeducationorgchanging-young-lives-2012
Think about the analytics products and initiatives reviewed above ndash where would you locate them on these dimensions
Expert-led enquiry
Student-led enquiry
Expert-led teaching
Student-led revision
Kno
wle
dge
co
-gen
erat
ion
an
d us
e
Pre-
scrib
ed
Kno
wle
dge
Teacher agency Student agency
Repetition Abstraction Acquisition
Authenticity Agency Identity
Teaching as learning design
The Knowledge-Agency Window
Learning analytics for this
Ruth Deakin Crick Univ Bristol Centre for Systems Learning amp Leadership ldquoPedagogy of Hoperdquo httplearningemergencenet20120921pedagogy-of-hope
analytics grounded in the principles of good
assessment for learning
(not summative assessment for
grading pupils teachers institutions or nations)
27
Assessment for Learning
28
httpassessment-reform-grouporg Few learning analytics are
currently able to take o board the richness of this
original conception of assessment for learning
Assessment for Learning
29
httpassessment-reform-grouporg
Assessment for Learning
30
httpassessment-reform-grouporg
To what extent could automated
feedback be designed and evaluated with
emotional impact in mind
Assessment for Learning
31
httpassessment-reform-grouporg
Can analytics identify proxies
for such advanced qualities
Assessment for Learning
32
httpassessment-reform-grouporg
How do we provide analytics feedback
that does not disempower and de-motivate struggling
learners
analytics for learning conversations
33
Socio-cultural discourse analysis (Mercer et al OU)
bull Disputational talk characterised by disagreement and individualised decision making
bull Cumulative talk in which speakers build positively but uncritically on what the others have said
bull Exploratory talk in which partners engage critically but constructively with each others ideas
34 Mercer N (2004) Sociocultural discourse analysis analysing classroom talk as a social mode of thinking Journal of Applied Linguistics 1(2) 137-168
bull Exploratory talk in which partners engage critically but constructively with each others ideas
bull Statements and suggestions are offered for joint consideration
bull These may be challenged and counter-challenged but challenges are justified and alternative hypotheses are offered
bull Partners all actively participate and opinions are sought and considered before decisions are jointly made
bull Compared with the other two types in Exploratory talk knowledge is made more publicly accountable and reasoning is more visible in the talk
35
Socio-cultural discourse analysis (Mercer et al OU)
Mercer N (2004) Sociocultural discourse analysis analysing classroom talk as a social mode of thinking Journal of Applied Linguistics 1(2) 137-168
Analytics for identifying Exploratory talk
36
Elluminate sessions can be very long ndash lasting for hours or even covering days of a conference
It would be useful if we could identify where quality learning conversations seem to be taking place so we can recommend those sessions and not have to sit through online chat about virtual biscuits
Ferguson R and Buckingham Shum S Learning analytics to identify exploratory dialogue within synchronous text chat 1st International Conference on Learning Analytics amp Knowledge (Banff Canada 27 Mar-1 Apr 2011)
Defining indicators of Exploratory Talk
37
Category Indicator Challenge But if have to respond my view Critique However Irsquom not sure maybe Discussion of resources
Have you read more links
Evaluation Good example good point Explanation Means that our goals Explicit reasoning Next step relates to thatrsquos why Justification I mean we learned we observed Reflections of perspectives of others
Agree here is another makes the point take your point your view
Extract classified as Exploratory Talk
38
Time Contribution 242 PM I hate talking -P My question was whether gadgets were just
basically widgets and we could embed them in various web sites like Netvibes Google Desktop etc
242 PM Thanks thats great I am sure I understood everything but looks inspiring
243 PM Yes why OU tools not generic tools
243 PM Issues of interoperability
243 PM The new SocialLearn site looks a lot like a corkboard where you can add various widgets similar to those existing web start pages
243 PM What if we end up with as many appsgadgets as we have social networks and then we need a recommender for the apps
243 PM My question was on the definition of the crowd in the wisdom of crowds we acsess in the service model
243 PM there are various different flavours of widget eg Google gadgets W3C widgets etc SocialLearn has gone for Google gadgets
-60
-40
-20
0
20
40
60
80
92
8 9
32
93
6
94
0
94
1
94
6
95
0
95
3
95
6
10
00
10
05
10
07
10
07
10
09
10
13
10
17
10
23
10
27
10
31
10
35
10
40
10
45
10
52
10
55
11
04
11
08
11
11
11
17
11
20
11
24
11
26
11
28
11
31
11
32
11
35
11
36
11
38
11
39
11
41
11
44
11
46
11
48
11
52
11
54
12
00
12
03
12
04
12
05
Average Exploratory
Discourse analytics on webinar textchat
Sheffield UK not as sunny as yesterday - still warm Greetings from Hong Kong Morning from Wiltshire sunny here
See you bye for now bye and thank you Bye all for now
Extensions to Ferguson R and Buckingham Shum S (2011) Learning Analytics to Identify Exploratory Dialogue within Synchronous Text Chat Proc 1st Int Conf Learning Analytics amp Knowledge Feb 27-Mar 1 2011 Banff ACM Press Eprint httporoopenacuk28955
Given a 25 hour webinar where in the live textchat were the most effective learning conversations Not at the start and end of a webinar but if we zoom in on a peakhellip
Discourse analytics on webinar textchat
-100
-50
0
50
100
92
8 9
40
9
50
1
000
1
007
1
017
1
031
1
045
1
104
1
117
1
126
1
132
1
138
1
144
1
152
1
203
Averag
Wei amp He extensions to Ferguson R and Buckingham Shum S (2011) Learning Analytics to Identify Exploratory Dialogue within Synchronous Text Chat Proc 1st Int Conf Learning Analytics amp Knowledge Feb 27-Mar 1 2011 Banff ACM Press Eprint httporoopenacuk28955
Classified as ldquoexploratory
talkrdquo
(more substantive for learning)
ldquonon-exploratoryrdquo
Given a 25 hour webinar where in the live textchat were the most effective learning conversations
De Liddo A Buckingham Shum S Quinto I Bachler M and Cannavacciuolo L Discourse-centric learning analytics 1st International Conference on Learning Analytics amp Knowledge (Banff 27 Mar-1 Apr 2011) httporoopenacuk25829
KMirsquos Cohere a web deliberation platform enabling semantic social network and discourse network analytics
Rebecca is playing the role of broker
connecting 2 peersrsquo contributions in meaningful ways
analytics for scholarly writing
42
Discourse analysis (Xerox Incremental Parser)
BACKGROUND KNOWLEDGE
Recent studies indicate hellip
hellip the previously proposed hellip
hellip is universally accepted
NOVELTY
new insights provide direct evidence
we suggest a new approach
results define a novel role
OPEN QUESTION hellip little is known hellip hellip role hellip has been elusive
Current data is insufficient hellip
GENERALIZING emerging as a promising approach Our understanding has grown exponentially growing recognition of the
importance
CONRASTING IDEAS hellip unorthodox view resolves hellip paradoxes hellip
In contrast with previous hypotheses
inconsistent with past findings
SIGNIFICANCE studies have provided important advances
Knowledge is crucial for understanding
valuable information from studies
SURPRISE We have recently observed surprisingly
We have identified unusual The recent discovery suggests intriguing roles
SUMMARIZING The goal of this study Here we show
Altogether our results indicate
Detection of salient sentences in scholarly reports based on the rhetorical signals authors use
Aacutegnes Saacutendor amp OLnet Project httpolnetorgnode512
De Liddo A Saacutendor Aacute and Buckingham Shum S Contested Collective Intelligence Rationale Technologies and a Human-Machine Annotation Study Computer Supported Cooperative Work 21 4-5 (2012) 417-448 httporoopenacuk31052
Human and machine analysis of a text for key contributions
19 sentences annotated 22 sentences annotated 11 sentences same as human annotation
71 sentences annotated 59 sentences annotated 42 sentences same as human annotation
Document 1
Document 2
httptechnologieskmiopenacukcohere20120109cohere-plus-automated-rhetorical-annotation De Liddo A Saacutendor Aacute and Buckingham Shum S Contested Collective Intelligence Rationale Technologies and a Human-Machine Annotation Study Computer Supported Cooperative Work 21 4-5 (2012) 417-448 httporoopenacuk31052
analytics for intepersonal networking
45
Semantic Social Network Analytics
De Liddo A Buckingham Shum S Quinto I Bachler M and Cannavacciuolo L Discourse-centric learning analytics 1st International Conference on Learning Analytics amp Knowledge (Banff 27 Mar-1 Apr 2011) httporoopenacuk25829
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Closing thoughts
54
55
ldquoThe basic question is not what can we measure
The basic question is
what does a good education look likerdquo
(Gardner Campbell)
httpchroniclecomblogstechtherapy20120502episode-95-learning-analytics-could-lead-to-wal-martification-of-college httplak12wikispacescomRecordings
56
Our analytics promote values pedagogy and assessment regimes
Are we clear which master
our analytics serve Are we happy to be judged by them
57
Will learning analytics merely turbocharge the current educational paradigm
mdash which is so often declared
not fit for purposehellip
58
hellipor will learning analytics reflect what we now know about designing authentic
engaged learning developing the new qualities that a
complex society demands
A learning analytics ecosystem
12
learners
educators
A learning analytics ecosystem
13
learners
educators
A learning analytics ecosystem
14
learners
educators
A learning analytics ecosystem
15
learners
educators
data curators translators
dashboard design team
data capture design team
Where did the data come from
16
learners
Where did the data come from
17
learners
researchers educators instructional designers
theories pedagogies
assessments tools
Where did the data come from
18
learners
researchers educators instructional designers
theories pedagogies
assessments tools
technologists
Data Intent
The map is not the territory Analytics are not the end but a means The goal is to optimize the whole system
19
learners
researchers educators instructional designers
theories pedagogies
assessments tools
desi
gn feedback
intent
outcome
Optimize the system for what
20
Same outcomes but higher scores
Learning Analytics as
Evolutionary Technology
bull more engaging bull better assessed bull better outcomes
bull deliverable at scale
21
New outcomes we couldnrsquot assess before
Learning Analytics as
Revolutionary Technology
bull learner behaviours quantifiable bull interpersonal networks quantifiable
bull discourse quantifiable bull moods and dispositions quantifiable
22
ldquoWe are preparing students for jobs that do not exist yet that will use technologies that have not been invented yet in order to solve problems that are not even problems yetrdquo
ldquoShift Happensrdquo httpshifthappenswikispacescom
23
Learning analytics for this
Learning analytics for this
ldquoWhile employers continue to demand high academic standards they also now want more They want people who can adapt see connections innovate communicate and work with others This is true in many areas of work The new knowledge-based economies in particular will increasingly depend on these abilities Many businesses are paying for courses to promote creative abilities to teach the skills and attitudes that are now essential for economic successhelliprdquo
All our Futures Creativity culture amp education May 1999 24
Learning analytics for this
25
Creativity Culture and Education (2009) Changing Young Lives 2012 Newcastle CCE httpwwwcreativitycultureeducationorgchanging-young-lives-2012
Think about the analytics products and initiatives reviewed above ndash where would you locate them on these dimensions
Expert-led enquiry
Student-led enquiry
Expert-led teaching
Student-led revision
Kno
wle
dge
co
-gen
erat
ion
an
d us
e
Pre-
scrib
ed
Kno
wle
dge
Teacher agency Student agency
Repetition Abstraction Acquisition
Authenticity Agency Identity
Teaching as learning design
The Knowledge-Agency Window
Learning analytics for this
Ruth Deakin Crick Univ Bristol Centre for Systems Learning amp Leadership ldquoPedagogy of Hoperdquo httplearningemergencenet20120921pedagogy-of-hope
analytics grounded in the principles of good
assessment for learning
(not summative assessment for
grading pupils teachers institutions or nations)
27
Assessment for Learning
28
httpassessment-reform-grouporg Few learning analytics are
currently able to take o board the richness of this
original conception of assessment for learning
Assessment for Learning
29
httpassessment-reform-grouporg
Assessment for Learning
30
httpassessment-reform-grouporg
To what extent could automated
feedback be designed and evaluated with
emotional impact in mind
Assessment for Learning
31
httpassessment-reform-grouporg
Can analytics identify proxies
for such advanced qualities
Assessment for Learning
32
httpassessment-reform-grouporg
How do we provide analytics feedback
that does not disempower and de-motivate struggling
learners
analytics for learning conversations
33
Socio-cultural discourse analysis (Mercer et al OU)
bull Disputational talk characterised by disagreement and individualised decision making
bull Cumulative talk in which speakers build positively but uncritically on what the others have said
bull Exploratory talk in which partners engage critically but constructively with each others ideas
34 Mercer N (2004) Sociocultural discourse analysis analysing classroom talk as a social mode of thinking Journal of Applied Linguistics 1(2) 137-168
bull Exploratory talk in which partners engage critically but constructively with each others ideas
bull Statements and suggestions are offered for joint consideration
bull These may be challenged and counter-challenged but challenges are justified and alternative hypotheses are offered
bull Partners all actively participate and opinions are sought and considered before decisions are jointly made
bull Compared with the other two types in Exploratory talk knowledge is made more publicly accountable and reasoning is more visible in the talk
35
Socio-cultural discourse analysis (Mercer et al OU)
Mercer N (2004) Sociocultural discourse analysis analysing classroom talk as a social mode of thinking Journal of Applied Linguistics 1(2) 137-168
Analytics for identifying Exploratory talk
36
Elluminate sessions can be very long ndash lasting for hours or even covering days of a conference
It would be useful if we could identify where quality learning conversations seem to be taking place so we can recommend those sessions and not have to sit through online chat about virtual biscuits
Ferguson R and Buckingham Shum S Learning analytics to identify exploratory dialogue within synchronous text chat 1st International Conference on Learning Analytics amp Knowledge (Banff Canada 27 Mar-1 Apr 2011)
Defining indicators of Exploratory Talk
37
Category Indicator Challenge But if have to respond my view Critique However Irsquom not sure maybe Discussion of resources
Have you read more links
Evaluation Good example good point Explanation Means that our goals Explicit reasoning Next step relates to thatrsquos why Justification I mean we learned we observed Reflections of perspectives of others
Agree here is another makes the point take your point your view
Extract classified as Exploratory Talk
38
Time Contribution 242 PM I hate talking -P My question was whether gadgets were just
basically widgets and we could embed them in various web sites like Netvibes Google Desktop etc
242 PM Thanks thats great I am sure I understood everything but looks inspiring
243 PM Yes why OU tools not generic tools
243 PM Issues of interoperability
243 PM The new SocialLearn site looks a lot like a corkboard where you can add various widgets similar to those existing web start pages
243 PM What if we end up with as many appsgadgets as we have social networks and then we need a recommender for the apps
243 PM My question was on the definition of the crowd in the wisdom of crowds we acsess in the service model
243 PM there are various different flavours of widget eg Google gadgets W3C widgets etc SocialLearn has gone for Google gadgets
-60
-40
-20
0
20
40
60
80
92
8 9
32
93
6
94
0
94
1
94
6
95
0
95
3
95
6
10
00
10
05
10
07
10
07
10
09
10
13
10
17
10
23
10
27
10
31
10
35
10
40
10
45
10
52
10
55
11
04
11
08
11
11
11
17
11
20
11
24
11
26
11
28
11
31
11
32
11
35
11
36
11
38
11
39
11
41
11
44
11
46
11
48
11
52
11
54
12
00
12
03
12
04
12
05
Average Exploratory
Discourse analytics on webinar textchat
Sheffield UK not as sunny as yesterday - still warm Greetings from Hong Kong Morning from Wiltshire sunny here
See you bye for now bye and thank you Bye all for now
Extensions to Ferguson R and Buckingham Shum S (2011) Learning Analytics to Identify Exploratory Dialogue within Synchronous Text Chat Proc 1st Int Conf Learning Analytics amp Knowledge Feb 27-Mar 1 2011 Banff ACM Press Eprint httporoopenacuk28955
Given a 25 hour webinar where in the live textchat were the most effective learning conversations Not at the start and end of a webinar but if we zoom in on a peakhellip
Discourse analytics on webinar textchat
-100
-50
0
50
100
92
8 9
40
9
50
1
000
1
007
1
017
1
031
1
045
1
104
1
117
1
126
1
132
1
138
1
144
1
152
1
203
Averag
Wei amp He extensions to Ferguson R and Buckingham Shum S (2011) Learning Analytics to Identify Exploratory Dialogue within Synchronous Text Chat Proc 1st Int Conf Learning Analytics amp Knowledge Feb 27-Mar 1 2011 Banff ACM Press Eprint httporoopenacuk28955
Classified as ldquoexploratory
talkrdquo
(more substantive for learning)
ldquonon-exploratoryrdquo
Given a 25 hour webinar where in the live textchat were the most effective learning conversations
De Liddo A Buckingham Shum S Quinto I Bachler M and Cannavacciuolo L Discourse-centric learning analytics 1st International Conference on Learning Analytics amp Knowledge (Banff 27 Mar-1 Apr 2011) httporoopenacuk25829
KMirsquos Cohere a web deliberation platform enabling semantic social network and discourse network analytics
Rebecca is playing the role of broker
connecting 2 peersrsquo contributions in meaningful ways
analytics for scholarly writing
42
Discourse analysis (Xerox Incremental Parser)
BACKGROUND KNOWLEDGE
Recent studies indicate hellip
hellip the previously proposed hellip
hellip is universally accepted
NOVELTY
new insights provide direct evidence
we suggest a new approach
results define a novel role
OPEN QUESTION hellip little is known hellip hellip role hellip has been elusive
Current data is insufficient hellip
GENERALIZING emerging as a promising approach Our understanding has grown exponentially growing recognition of the
importance
CONRASTING IDEAS hellip unorthodox view resolves hellip paradoxes hellip
In contrast with previous hypotheses
inconsistent with past findings
SIGNIFICANCE studies have provided important advances
Knowledge is crucial for understanding
valuable information from studies
SURPRISE We have recently observed surprisingly
We have identified unusual The recent discovery suggests intriguing roles
SUMMARIZING The goal of this study Here we show
Altogether our results indicate
Detection of salient sentences in scholarly reports based on the rhetorical signals authors use
Aacutegnes Saacutendor amp OLnet Project httpolnetorgnode512
De Liddo A Saacutendor Aacute and Buckingham Shum S Contested Collective Intelligence Rationale Technologies and a Human-Machine Annotation Study Computer Supported Cooperative Work 21 4-5 (2012) 417-448 httporoopenacuk31052
Human and machine analysis of a text for key contributions
19 sentences annotated 22 sentences annotated 11 sentences same as human annotation
71 sentences annotated 59 sentences annotated 42 sentences same as human annotation
Document 1
Document 2
httptechnologieskmiopenacukcohere20120109cohere-plus-automated-rhetorical-annotation De Liddo A Saacutendor Aacute and Buckingham Shum S Contested Collective Intelligence Rationale Technologies and a Human-Machine Annotation Study Computer Supported Cooperative Work 21 4-5 (2012) 417-448 httporoopenacuk31052
analytics for intepersonal networking
45
Semantic Social Network Analytics
De Liddo A Buckingham Shum S Quinto I Bachler M and Cannavacciuolo L Discourse-centric learning analytics 1st International Conference on Learning Analytics amp Knowledge (Banff 27 Mar-1 Apr 2011) httporoopenacuk25829
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Closing thoughts
54
55
ldquoThe basic question is not what can we measure
The basic question is
what does a good education look likerdquo
(Gardner Campbell)
httpchroniclecomblogstechtherapy20120502episode-95-learning-analytics-could-lead-to-wal-martification-of-college httplak12wikispacescomRecordings
56
Our analytics promote values pedagogy and assessment regimes
Are we clear which master
our analytics serve Are we happy to be judged by them
57
Will learning analytics merely turbocharge the current educational paradigm
mdash which is so often declared
not fit for purposehellip
58
hellipor will learning analytics reflect what we now know about designing authentic
engaged learning developing the new qualities that a
complex society demands
A learning analytics ecosystem
13
learners
educators
A learning analytics ecosystem
14
learners
educators
A learning analytics ecosystem
15
learners
educators
data curators translators
dashboard design team
data capture design team
Where did the data come from
16
learners
Where did the data come from
17
learners
researchers educators instructional designers
theories pedagogies
assessments tools
Where did the data come from
18
learners
researchers educators instructional designers
theories pedagogies
assessments tools
technologists
Data Intent
The map is not the territory Analytics are not the end but a means The goal is to optimize the whole system
19
learners
researchers educators instructional designers
theories pedagogies
assessments tools
desi
gn feedback
intent
outcome
Optimize the system for what
20
Same outcomes but higher scores
Learning Analytics as
Evolutionary Technology
bull more engaging bull better assessed bull better outcomes
bull deliverable at scale
21
New outcomes we couldnrsquot assess before
Learning Analytics as
Revolutionary Technology
bull learner behaviours quantifiable bull interpersonal networks quantifiable
bull discourse quantifiable bull moods and dispositions quantifiable
22
ldquoWe are preparing students for jobs that do not exist yet that will use technologies that have not been invented yet in order to solve problems that are not even problems yetrdquo
ldquoShift Happensrdquo httpshifthappenswikispacescom
23
Learning analytics for this
Learning analytics for this
ldquoWhile employers continue to demand high academic standards they also now want more They want people who can adapt see connections innovate communicate and work with others This is true in many areas of work The new knowledge-based economies in particular will increasingly depend on these abilities Many businesses are paying for courses to promote creative abilities to teach the skills and attitudes that are now essential for economic successhelliprdquo
All our Futures Creativity culture amp education May 1999 24
Learning analytics for this
25
Creativity Culture and Education (2009) Changing Young Lives 2012 Newcastle CCE httpwwwcreativitycultureeducationorgchanging-young-lives-2012
Think about the analytics products and initiatives reviewed above ndash where would you locate them on these dimensions
Expert-led enquiry
Student-led enquiry
Expert-led teaching
Student-led revision
Kno
wle
dge
co
-gen
erat
ion
an
d us
e
Pre-
scrib
ed
Kno
wle
dge
Teacher agency Student agency
Repetition Abstraction Acquisition
Authenticity Agency Identity
Teaching as learning design
The Knowledge-Agency Window
Learning analytics for this
Ruth Deakin Crick Univ Bristol Centre for Systems Learning amp Leadership ldquoPedagogy of Hoperdquo httplearningemergencenet20120921pedagogy-of-hope
analytics grounded in the principles of good
assessment for learning
(not summative assessment for
grading pupils teachers institutions or nations)
27
Assessment for Learning
28
httpassessment-reform-grouporg Few learning analytics are
currently able to take o board the richness of this
original conception of assessment for learning
Assessment for Learning
29
httpassessment-reform-grouporg
Assessment for Learning
30
httpassessment-reform-grouporg
To what extent could automated
feedback be designed and evaluated with
emotional impact in mind
Assessment for Learning
31
httpassessment-reform-grouporg
Can analytics identify proxies
for such advanced qualities
Assessment for Learning
32
httpassessment-reform-grouporg
How do we provide analytics feedback
that does not disempower and de-motivate struggling
learners
analytics for learning conversations
33
Socio-cultural discourse analysis (Mercer et al OU)
bull Disputational talk characterised by disagreement and individualised decision making
bull Cumulative talk in which speakers build positively but uncritically on what the others have said
bull Exploratory talk in which partners engage critically but constructively with each others ideas
34 Mercer N (2004) Sociocultural discourse analysis analysing classroom talk as a social mode of thinking Journal of Applied Linguistics 1(2) 137-168
bull Exploratory talk in which partners engage critically but constructively with each others ideas
bull Statements and suggestions are offered for joint consideration
bull These may be challenged and counter-challenged but challenges are justified and alternative hypotheses are offered
bull Partners all actively participate and opinions are sought and considered before decisions are jointly made
bull Compared with the other two types in Exploratory talk knowledge is made more publicly accountable and reasoning is more visible in the talk
35
Socio-cultural discourse analysis (Mercer et al OU)
Mercer N (2004) Sociocultural discourse analysis analysing classroom talk as a social mode of thinking Journal of Applied Linguistics 1(2) 137-168
Analytics for identifying Exploratory talk
36
Elluminate sessions can be very long ndash lasting for hours or even covering days of a conference
It would be useful if we could identify where quality learning conversations seem to be taking place so we can recommend those sessions and not have to sit through online chat about virtual biscuits
Ferguson R and Buckingham Shum S Learning analytics to identify exploratory dialogue within synchronous text chat 1st International Conference on Learning Analytics amp Knowledge (Banff Canada 27 Mar-1 Apr 2011)
Defining indicators of Exploratory Talk
37
Category Indicator Challenge But if have to respond my view Critique However Irsquom not sure maybe Discussion of resources
Have you read more links
Evaluation Good example good point Explanation Means that our goals Explicit reasoning Next step relates to thatrsquos why Justification I mean we learned we observed Reflections of perspectives of others
Agree here is another makes the point take your point your view
Extract classified as Exploratory Talk
38
Time Contribution 242 PM I hate talking -P My question was whether gadgets were just
basically widgets and we could embed them in various web sites like Netvibes Google Desktop etc
242 PM Thanks thats great I am sure I understood everything but looks inspiring
243 PM Yes why OU tools not generic tools
243 PM Issues of interoperability
243 PM The new SocialLearn site looks a lot like a corkboard where you can add various widgets similar to those existing web start pages
243 PM What if we end up with as many appsgadgets as we have social networks and then we need a recommender for the apps
243 PM My question was on the definition of the crowd in the wisdom of crowds we acsess in the service model
243 PM there are various different flavours of widget eg Google gadgets W3C widgets etc SocialLearn has gone for Google gadgets
-60
-40
-20
0
20
40
60
80
92
8 9
32
93
6
94
0
94
1
94
6
95
0
95
3
95
6
10
00
10
05
10
07
10
07
10
09
10
13
10
17
10
23
10
27
10
31
10
35
10
40
10
45
10
52
10
55
11
04
11
08
11
11
11
17
11
20
11
24
11
26
11
28
11
31
11
32
11
35
11
36
11
38
11
39
11
41
11
44
11
46
11
48
11
52
11
54
12
00
12
03
12
04
12
05
Average Exploratory
Discourse analytics on webinar textchat
Sheffield UK not as sunny as yesterday - still warm Greetings from Hong Kong Morning from Wiltshire sunny here
See you bye for now bye and thank you Bye all for now
Extensions to Ferguson R and Buckingham Shum S (2011) Learning Analytics to Identify Exploratory Dialogue within Synchronous Text Chat Proc 1st Int Conf Learning Analytics amp Knowledge Feb 27-Mar 1 2011 Banff ACM Press Eprint httporoopenacuk28955
Given a 25 hour webinar where in the live textchat were the most effective learning conversations Not at the start and end of a webinar but if we zoom in on a peakhellip
Discourse analytics on webinar textchat
-100
-50
0
50
100
92
8 9
40
9
50
1
000
1
007
1
017
1
031
1
045
1
104
1
117
1
126
1
132
1
138
1
144
1
152
1
203
Averag
Wei amp He extensions to Ferguson R and Buckingham Shum S (2011) Learning Analytics to Identify Exploratory Dialogue within Synchronous Text Chat Proc 1st Int Conf Learning Analytics amp Knowledge Feb 27-Mar 1 2011 Banff ACM Press Eprint httporoopenacuk28955
Classified as ldquoexploratory
talkrdquo
(more substantive for learning)
ldquonon-exploratoryrdquo
Given a 25 hour webinar where in the live textchat were the most effective learning conversations
De Liddo A Buckingham Shum S Quinto I Bachler M and Cannavacciuolo L Discourse-centric learning analytics 1st International Conference on Learning Analytics amp Knowledge (Banff 27 Mar-1 Apr 2011) httporoopenacuk25829
KMirsquos Cohere a web deliberation platform enabling semantic social network and discourse network analytics
Rebecca is playing the role of broker
connecting 2 peersrsquo contributions in meaningful ways
analytics for scholarly writing
42
Discourse analysis (Xerox Incremental Parser)
BACKGROUND KNOWLEDGE
Recent studies indicate hellip
hellip the previously proposed hellip
hellip is universally accepted
NOVELTY
new insights provide direct evidence
we suggest a new approach
results define a novel role
OPEN QUESTION hellip little is known hellip hellip role hellip has been elusive
Current data is insufficient hellip
GENERALIZING emerging as a promising approach Our understanding has grown exponentially growing recognition of the
importance
CONRASTING IDEAS hellip unorthodox view resolves hellip paradoxes hellip
In contrast with previous hypotheses
inconsistent with past findings
SIGNIFICANCE studies have provided important advances
Knowledge is crucial for understanding
valuable information from studies
SURPRISE We have recently observed surprisingly
We have identified unusual The recent discovery suggests intriguing roles
SUMMARIZING The goal of this study Here we show
Altogether our results indicate
Detection of salient sentences in scholarly reports based on the rhetorical signals authors use
Aacutegnes Saacutendor amp OLnet Project httpolnetorgnode512
De Liddo A Saacutendor Aacute and Buckingham Shum S Contested Collective Intelligence Rationale Technologies and a Human-Machine Annotation Study Computer Supported Cooperative Work 21 4-5 (2012) 417-448 httporoopenacuk31052
Human and machine analysis of a text for key contributions
19 sentences annotated 22 sentences annotated 11 sentences same as human annotation
71 sentences annotated 59 sentences annotated 42 sentences same as human annotation
Document 1
Document 2
httptechnologieskmiopenacukcohere20120109cohere-plus-automated-rhetorical-annotation De Liddo A Saacutendor Aacute and Buckingham Shum S Contested Collective Intelligence Rationale Technologies and a Human-Machine Annotation Study Computer Supported Cooperative Work 21 4-5 (2012) 417-448 httporoopenacuk31052
analytics for intepersonal networking
45
Semantic Social Network Analytics
De Liddo A Buckingham Shum S Quinto I Bachler M and Cannavacciuolo L Discourse-centric learning analytics 1st International Conference on Learning Analytics amp Knowledge (Banff 27 Mar-1 Apr 2011) httporoopenacuk25829
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Closing thoughts
54
55
ldquoThe basic question is not what can we measure
The basic question is
what does a good education look likerdquo
(Gardner Campbell)
httpchroniclecomblogstechtherapy20120502episode-95-learning-analytics-could-lead-to-wal-martification-of-college httplak12wikispacescomRecordings
56
Our analytics promote values pedagogy and assessment regimes
Are we clear which master
our analytics serve Are we happy to be judged by them
57
Will learning analytics merely turbocharge the current educational paradigm
mdash which is so often declared
not fit for purposehellip
58
hellipor will learning analytics reflect what we now know about designing authentic
engaged learning developing the new qualities that a
complex society demands
A learning analytics ecosystem
14
learners
educators
A learning analytics ecosystem
15
learners
educators
data curators translators
dashboard design team
data capture design team
Where did the data come from
16
learners
Where did the data come from
17
learners
researchers educators instructional designers
theories pedagogies
assessments tools
Where did the data come from
18
learners
researchers educators instructional designers
theories pedagogies
assessments tools
technologists
Data Intent
The map is not the territory Analytics are not the end but a means The goal is to optimize the whole system
19
learners
researchers educators instructional designers
theories pedagogies
assessments tools
desi
gn feedback
intent
outcome
Optimize the system for what
20
Same outcomes but higher scores
Learning Analytics as
Evolutionary Technology
bull more engaging bull better assessed bull better outcomes
bull deliverable at scale
21
New outcomes we couldnrsquot assess before
Learning Analytics as
Revolutionary Technology
bull learner behaviours quantifiable bull interpersonal networks quantifiable
bull discourse quantifiable bull moods and dispositions quantifiable
22
ldquoWe are preparing students for jobs that do not exist yet that will use technologies that have not been invented yet in order to solve problems that are not even problems yetrdquo
ldquoShift Happensrdquo httpshifthappenswikispacescom
23
Learning analytics for this
Learning analytics for this
ldquoWhile employers continue to demand high academic standards they also now want more They want people who can adapt see connections innovate communicate and work with others This is true in many areas of work The new knowledge-based economies in particular will increasingly depend on these abilities Many businesses are paying for courses to promote creative abilities to teach the skills and attitudes that are now essential for economic successhelliprdquo
All our Futures Creativity culture amp education May 1999 24
Learning analytics for this
25
Creativity Culture and Education (2009) Changing Young Lives 2012 Newcastle CCE httpwwwcreativitycultureeducationorgchanging-young-lives-2012
Think about the analytics products and initiatives reviewed above ndash where would you locate them on these dimensions
Expert-led enquiry
Student-led enquiry
Expert-led teaching
Student-led revision
Kno
wle
dge
co
-gen
erat
ion
an
d us
e
Pre-
scrib
ed
Kno
wle
dge
Teacher agency Student agency
Repetition Abstraction Acquisition
Authenticity Agency Identity
Teaching as learning design
The Knowledge-Agency Window
Learning analytics for this
Ruth Deakin Crick Univ Bristol Centre for Systems Learning amp Leadership ldquoPedagogy of Hoperdquo httplearningemergencenet20120921pedagogy-of-hope
analytics grounded in the principles of good
assessment for learning
(not summative assessment for
grading pupils teachers institutions or nations)
27
Assessment for Learning
28
httpassessment-reform-grouporg Few learning analytics are
currently able to take o board the richness of this
original conception of assessment for learning
Assessment for Learning
29
httpassessment-reform-grouporg
Assessment for Learning
30
httpassessment-reform-grouporg
To what extent could automated
feedback be designed and evaluated with
emotional impact in mind
Assessment for Learning
31
httpassessment-reform-grouporg
Can analytics identify proxies
for such advanced qualities
Assessment for Learning
32
httpassessment-reform-grouporg
How do we provide analytics feedback
that does not disempower and de-motivate struggling
learners
analytics for learning conversations
33
Socio-cultural discourse analysis (Mercer et al OU)
bull Disputational talk characterised by disagreement and individualised decision making
bull Cumulative talk in which speakers build positively but uncritically on what the others have said
bull Exploratory talk in which partners engage critically but constructively with each others ideas
34 Mercer N (2004) Sociocultural discourse analysis analysing classroom talk as a social mode of thinking Journal of Applied Linguistics 1(2) 137-168
bull Exploratory talk in which partners engage critically but constructively with each others ideas
bull Statements and suggestions are offered for joint consideration
bull These may be challenged and counter-challenged but challenges are justified and alternative hypotheses are offered
bull Partners all actively participate and opinions are sought and considered before decisions are jointly made
bull Compared with the other two types in Exploratory talk knowledge is made more publicly accountable and reasoning is more visible in the talk
35
Socio-cultural discourse analysis (Mercer et al OU)
Mercer N (2004) Sociocultural discourse analysis analysing classroom talk as a social mode of thinking Journal of Applied Linguistics 1(2) 137-168
Analytics for identifying Exploratory talk
36
Elluminate sessions can be very long ndash lasting for hours or even covering days of a conference
It would be useful if we could identify where quality learning conversations seem to be taking place so we can recommend those sessions and not have to sit through online chat about virtual biscuits
Ferguson R and Buckingham Shum S Learning analytics to identify exploratory dialogue within synchronous text chat 1st International Conference on Learning Analytics amp Knowledge (Banff Canada 27 Mar-1 Apr 2011)
Defining indicators of Exploratory Talk
37
Category Indicator Challenge But if have to respond my view Critique However Irsquom not sure maybe Discussion of resources
Have you read more links
Evaluation Good example good point Explanation Means that our goals Explicit reasoning Next step relates to thatrsquos why Justification I mean we learned we observed Reflections of perspectives of others
Agree here is another makes the point take your point your view
Extract classified as Exploratory Talk
38
Time Contribution 242 PM I hate talking -P My question was whether gadgets were just
basically widgets and we could embed them in various web sites like Netvibes Google Desktop etc
242 PM Thanks thats great I am sure I understood everything but looks inspiring
243 PM Yes why OU tools not generic tools
243 PM Issues of interoperability
243 PM The new SocialLearn site looks a lot like a corkboard where you can add various widgets similar to those existing web start pages
243 PM What if we end up with as many appsgadgets as we have social networks and then we need a recommender for the apps
243 PM My question was on the definition of the crowd in the wisdom of crowds we acsess in the service model
243 PM there are various different flavours of widget eg Google gadgets W3C widgets etc SocialLearn has gone for Google gadgets
-60
-40
-20
0
20
40
60
80
92
8 9
32
93
6
94
0
94
1
94
6
95
0
95
3
95
6
10
00
10
05
10
07
10
07
10
09
10
13
10
17
10
23
10
27
10
31
10
35
10
40
10
45
10
52
10
55
11
04
11
08
11
11
11
17
11
20
11
24
11
26
11
28
11
31
11
32
11
35
11
36
11
38
11
39
11
41
11
44
11
46
11
48
11
52
11
54
12
00
12
03
12
04
12
05
Average Exploratory
Discourse analytics on webinar textchat
Sheffield UK not as sunny as yesterday - still warm Greetings from Hong Kong Morning from Wiltshire sunny here
See you bye for now bye and thank you Bye all for now
Extensions to Ferguson R and Buckingham Shum S (2011) Learning Analytics to Identify Exploratory Dialogue within Synchronous Text Chat Proc 1st Int Conf Learning Analytics amp Knowledge Feb 27-Mar 1 2011 Banff ACM Press Eprint httporoopenacuk28955
Given a 25 hour webinar where in the live textchat were the most effective learning conversations Not at the start and end of a webinar but if we zoom in on a peakhellip
Discourse analytics on webinar textchat
-100
-50
0
50
100
92
8 9
40
9
50
1
000
1
007
1
017
1
031
1
045
1
104
1
117
1
126
1
132
1
138
1
144
1
152
1
203
Averag
Wei amp He extensions to Ferguson R and Buckingham Shum S (2011) Learning Analytics to Identify Exploratory Dialogue within Synchronous Text Chat Proc 1st Int Conf Learning Analytics amp Knowledge Feb 27-Mar 1 2011 Banff ACM Press Eprint httporoopenacuk28955
Classified as ldquoexploratory
talkrdquo
(more substantive for learning)
ldquonon-exploratoryrdquo
Given a 25 hour webinar where in the live textchat were the most effective learning conversations
De Liddo A Buckingham Shum S Quinto I Bachler M and Cannavacciuolo L Discourse-centric learning analytics 1st International Conference on Learning Analytics amp Knowledge (Banff 27 Mar-1 Apr 2011) httporoopenacuk25829
KMirsquos Cohere a web deliberation platform enabling semantic social network and discourse network analytics
Rebecca is playing the role of broker
connecting 2 peersrsquo contributions in meaningful ways
analytics for scholarly writing
42
Discourse analysis (Xerox Incremental Parser)
BACKGROUND KNOWLEDGE
Recent studies indicate hellip
hellip the previously proposed hellip
hellip is universally accepted
NOVELTY
new insights provide direct evidence
we suggest a new approach
results define a novel role
OPEN QUESTION hellip little is known hellip hellip role hellip has been elusive
Current data is insufficient hellip
GENERALIZING emerging as a promising approach Our understanding has grown exponentially growing recognition of the
importance
CONRASTING IDEAS hellip unorthodox view resolves hellip paradoxes hellip
In contrast with previous hypotheses
inconsistent with past findings
SIGNIFICANCE studies have provided important advances
Knowledge is crucial for understanding
valuable information from studies
SURPRISE We have recently observed surprisingly
We have identified unusual The recent discovery suggests intriguing roles
SUMMARIZING The goal of this study Here we show
Altogether our results indicate
Detection of salient sentences in scholarly reports based on the rhetorical signals authors use
Aacutegnes Saacutendor amp OLnet Project httpolnetorgnode512
De Liddo A Saacutendor Aacute and Buckingham Shum S Contested Collective Intelligence Rationale Technologies and a Human-Machine Annotation Study Computer Supported Cooperative Work 21 4-5 (2012) 417-448 httporoopenacuk31052
Human and machine analysis of a text for key contributions
19 sentences annotated 22 sentences annotated 11 sentences same as human annotation
71 sentences annotated 59 sentences annotated 42 sentences same as human annotation
Document 1
Document 2
httptechnologieskmiopenacukcohere20120109cohere-plus-automated-rhetorical-annotation De Liddo A Saacutendor Aacute and Buckingham Shum S Contested Collective Intelligence Rationale Technologies and a Human-Machine Annotation Study Computer Supported Cooperative Work 21 4-5 (2012) 417-448 httporoopenacuk31052
analytics for intepersonal networking
45
Semantic Social Network Analytics
De Liddo A Buckingham Shum S Quinto I Bachler M and Cannavacciuolo L Discourse-centric learning analytics 1st International Conference on Learning Analytics amp Knowledge (Banff 27 Mar-1 Apr 2011) httporoopenacuk25829
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Closing thoughts
54
55
ldquoThe basic question is not what can we measure
The basic question is
what does a good education look likerdquo
(Gardner Campbell)
httpchroniclecomblogstechtherapy20120502episode-95-learning-analytics-could-lead-to-wal-martification-of-college httplak12wikispacescomRecordings
56
Our analytics promote values pedagogy and assessment regimes
Are we clear which master
our analytics serve Are we happy to be judged by them
57
Will learning analytics merely turbocharge the current educational paradigm
mdash which is so often declared
not fit for purposehellip
58
hellipor will learning analytics reflect what we now know about designing authentic
engaged learning developing the new qualities that a
complex society demands
A learning analytics ecosystem
15
learners
educators
data curators translators
dashboard design team
data capture design team
Where did the data come from
16
learners
Where did the data come from
17
learners
researchers educators instructional designers
theories pedagogies
assessments tools
Where did the data come from
18
learners
researchers educators instructional designers
theories pedagogies
assessments tools
technologists
Data Intent
The map is not the territory Analytics are not the end but a means The goal is to optimize the whole system
19
learners
researchers educators instructional designers
theories pedagogies
assessments tools
desi
gn feedback
intent
outcome
Optimize the system for what
20
Same outcomes but higher scores
Learning Analytics as
Evolutionary Technology
bull more engaging bull better assessed bull better outcomes
bull deliverable at scale
21
New outcomes we couldnrsquot assess before
Learning Analytics as
Revolutionary Technology
bull learner behaviours quantifiable bull interpersonal networks quantifiable
bull discourse quantifiable bull moods and dispositions quantifiable
22
ldquoWe are preparing students for jobs that do not exist yet that will use technologies that have not been invented yet in order to solve problems that are not even problems yetrdquo
ldquoShift Happensrdquo httpshifthappenswikispacescom
23
Learning analytics for this
Learning analytics for this
ldquoWhile employers continue to demand high academic standards they also now want more They want people who can adapt see connections innovate communicate and work with others This is true in many areas of work The new knowledge-based economies in particular will increasingly depend on these abilities Many businesses are paying for courses to promote creative abilities to teach the skills and attitudes that are now essential for economic successhelliprdquo
All our Futures Creativity culture amp education May 1999 24
Learning analytics for this
25
Creativity Culture and Education (2009) Changing Young Lives 2012 Newcastle CCE httpwwwcreativitycultureeducationorgchanging-young-lives-2012
Think about the analytics products and initiatives reviewed above ndash where would you locate them on these dimensions
Expert-led enquiry
Student-led enquiry
Expert-led teaching
Student-led revision
Kno
wle
dge
co
-gen
erat
ion
an
d us
e
Pre-
scrib
ed
Kno
wle
dge
Teacher agency Student agency
Repetition Abstraction Acquisition
Authenticity Agency Identity
Teaching as learning design
The Knowledge-Agency Window
Learning analytics for this
Ruth Deakin Crick Univ Bristol Centre for Systems Learning amp Leadership ldquoPedagogy of Hoperdquo httplearningemergencenet20120921pedagogy-of-hope
analytics grounded in the principles of good
assessment for learning
(not summative assessment for
grading pupils teachers institutions or nations)
27
Assessment for Learning
28
httpassessment-reform-grouporg Few learning analytics are
currently able to take o board the richness of this
original conception of assessment for learning
Assessment for Learning
29
httpassessment-reform-grouporg
Assessment for Learning
30
httpassessment-reform-grouporg
To what extent could automated
feedback be designed and evaluated with
emotional impact in mind
Assessment for Learning
31
httpassessment-reform-grouporg
Can analytics identify proxies
for such advanced qualities
Assessment for Learning
32
httpassessment-reform-grouporg
How do we provide analytics feedback
that does not disempower and de-motivate struggling
learners
analytics for learning conversations
33
Socio-cultural discourse analysis (Mercer et al OU)
bull Disputational talk characterised by disagreement and individualised decision making
bull Cumulative talk in which speakers build positively but uncritically on what the others have said
bull Exploratory talk in which partners engage critically but constructively with each others ideas
34 Mercer N (2004) Sociocultural discourse analysis analysing classroom talk as a social mode of thinking Journal of Applied Linguistics 1(2) 137-168
bull Exploratory talk in which partners engage critically but constructively with each others ideas
bull Statements and suggestions are offered for joint consideration
bull These may be challenged and counter-challenged but challenges are justified and alternative hypotheses are offered
bull Partners all actively participate and opinions are sought and considered before decisions are jointly made
bull Compared with the other two types in Exploratory talk knowledge is made more publicly accountable and reasoning is more visible in the talk
35
Socio-cultural discourse analysis (Mercer et al OU)
Mercer N (2004) Sociocultural discourse analysis analysing classroom talk as a social mode of thinking Journal of Applied Linguistics 1(2) 137-168
Analytics for identifying Exploratory talk
36
Elluminate sessions can be very long ndash lasting for hours or even covering days of a conference
It would be useful if we could identify where quality learning conversations seem to be taking place so we can recommend those sessions and not have to sit through online chat about virtual biscuits
Ferguson R and Buckingham Shum S Learning analytics to identify exploratory dialogue within synchronous text chat 1st International Conference on Learning Analytics amp Knowledge (Banff Canada 27 Mar-1 Apr 2011)
Defining indicators of Exploratory Talk
37
Category Indicator Challenge But if have to respond my view Critique However Irsquom not sure maybe Discussion of resources
Have you read more links
Evaluation Good example good point Explanation Means that our goals Explicit reasoning Next step relates to thatrsquos why Justification I mean we learned we observed Reflections of perspectives of others
Agree here is another makes the point take your point your view
Extract classified as Exploratory Talk
38
Time Contribution 242 PM I hate talking -P My question was whether gadgets were just
basically widgets and we could embed them in various web sites like Netvibes Google Desktop etc
242 PM Thanks thats great I am sure I understood everything but looks inspiring
243 PM Yes why OU tools not generic tools
243 PM Issues of interoperability
243 PM The new SocialLearn site looks a lot like a corkboard where you can add various widgets similar to those existing web start pages
243 PM What if we end up with as many appsgadgets as we have social networks and then we need a recommender for the apps
243 PM My question was on the definition of the crowd in the wisdom of crowds we acsess in the service model
243 PM there are various different flavours of widget eg Google gadgets W3C widgets etc SocialLearn has gone for Google gadgets
-60
-40
-20
0
20
40
60
80
92
8 9
32
93
6
94
0
94
1
94
6
95
0
95
3
95
6
10
00
10
05
10
07
10
07
10
09
10
13
10
17
10
23
10
27
10
31
10
35
10
40
10
45
10
52
10
55
11
04
11
08
11
11
11
17
11
20
11
24
11
26
11
28
11
31
11
32
11
35
11
36
11
38
11
39
11
41
11
44
11
46
11
48
11
52
11
54
12
00
12
03
12
04
12
05
Average Exploratory
Discourse analytics on webinar textchat
Sheffield UK not as sunny as yesterday - still warm Greetings from Hong Kong Morning from Wiltshire sunny here
See you bye for now bye and thank you Bye all for now
Extensions to Ferguson R and Buckingham Shum S (2011) Learning Analytics to Identify Exploratory Dialogue within Synchronous Text Chat Proc 1st Int Conf Learning Analytics amp Knowledge Feb 27-Mar 1 2011 Banff ACM Press Eprint httporoopenacuk28955
Given a 25 hour webinar where in the live textchat were the most effective learning conversations Not at the start and end of a webinar but if we zoom in on a peakhellip
Discourse analytics on webinar textchat
-100
-50
0
50
100
92
8 9
40
9
50
1
000
1
007
1
017
1
031
1
045
1
104
1
117
1
126
1
132
1
138
1
144
1
152
1
203
Averag
Wei amp He extensions to Ferguson R and Buckingham Shum S (2011) Learning Analytics to Identify Exploratory Dialogue within Synchronous Text Chat Proc 1st Int Conf Learning Analytics amp Knowledge Feb 27-Mar 1 2011 Banff ACM Press Eprint httporoopenacuk28955
Classified as ldquoexploratory
talkrdquo
(more substantive for learning)
ldquonon-exploratoryrdquo
Given a 25 hour webinar where in the live textchat were the most effective learning conversations
De Liddo A Buckingham Shum S Quinto I Bachler M and Cannavacciuolo L Discourse-centric learning analytics 1st International Conference on Learning Analytics amp Knowledge (Banff 27 Mar-1 Apr 2011) httporoopenacuk25829
KMirsquos Cohere a web deliberation platform enabling semantic social network and discourse network analytics
Rebecca is playing the role of broker
connecting 2 peersrsquo contributions in meaningful ways
analytics for scholarly writing
42
Discourse analysis (Xerox Incremental Parser)
BACKGROUND KNOWLEDGE
Recent studies indicate hellip
hellip the previously proposed hellip
hellip is universally accepted
NOVELTY
new insights provide direct evidence
we suggest a new approach
results define a novel role
OPEN QUESTION hellip little is known hellip hellip role hellip has been elusive
Current data is insufficient hellip
GENERALIZING emerging as a promising approach Our understanding has grown exponentially growing recognition of the
importance
CONRASTING IDEAS hellip unorthodox view resolves hellip paradoxes hellip
In contrast with previous hypotheses
inconsistent with past findings
SIGNIFICANCE studies have provided important advances
Knowledge is crucial for understanding
valuable information from studies
SURPRISE We have recently observed surprisingly
We have identified unusual The recent discovery suggests intriguing roles
SUMMARIZING The goal of this study Here we show
Altogether our results indicate
Detection of salient sentences in scholarly reports based on the rhetorical signals authors use
Aacutegnes Saacutendor amp OLnet Project httpolnetorgnode512
De Liddo A Saacutendor Aacute and Buckingham Shum S Contested Collective Intelligence Rationale Technologies and a Human-Machine Annotation Study Computer Supported Cooperative Work 21 4-5 (2012) 417-448 httporoopenacuk31052
Human and machine analysis of a text for key contributions
19 sentences annotated 22 sentences annotated 11 sentences same as human annotation
71 sentences annotated 59 sentences annotated 42 sentences same as human annotation
Document 1
Document 2
httptechnologieskmiopenacukcohere20120109cohere-plus-automated-rhetorical-annotation De Liddo A Saacutendor Aacute and Buckingham Shum S Contested Collective Intelligence Rationale Technologies and a Human-Machine Annotation Study Computer Supported Cooperative Work 21 4-5 (2012) 417-448 httporoopenacuk31052
analytics for intepersonal networking
45
Semantic Social Network Analytics
De Liddo A Buckingham Shum S Quinto I Bachler M and Cannavacciuolo L Discourse-centric learning analytics 1st International Conference on Learning Analytics amp Knowledge (Banff 27 Mar-1 Apr 2011) httporoopenacuk25829
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Closing thoughts
54
55
ldquoThe basic question is not what can we measure
The basic question is
what does a good education look likerdquo
(Gardner Campbell)
httpchroniclecomblogstechtherapy20120502episode-95-learning-analytics-could-lead-to-wal-martification-of-college httplak12wikispacescomRecordings
56
Our analytics promote values pedagogy and assessment regimes
Are we clear which master
our analytics serve Are we happy to be judged by them
57
Will learning analytics merely turbocharge the current educational paradigm
mdash which is so often declared
not fit for purposehellip
58
hellipor will learning analytics reflect what we now know about designing authentic
engaged learning developing the new qualities that a
complex society demands
Where did the data come from
16
learners
Where did the data come from
17
learners
researchers educators instructional designers
theories pedagogies
assessments tools
Where did the data come from
18
learners
researchers educators instructional designers
theories pedagogies
assessments tools
technologists
Data Intent
The map is not the territory Analytics are not the end but a means The goal is to optimize the whole system
19
learners
researchers educators instructional designers
theories pedagogies
assessments tools
desi
gn feedback
intent
outcome
Optimize the system for what
20
Same outcomes but higher scores
Learning Analytics as
Evolutionary Technology
bull more engaging bull better assessed bull better outcomes
bull deliverable at scale
21
New outcomes we couldnrsquot assess before
Learning Analytics as
Revolutionary Technology
bull learner behaviours quantifiable bull interpersonal networks quantifiable
bull discourse quantifiable bull moods and dispositions quantifiable
22
ldquoWe are preparing students for jobs that do not exist yet that will use technologies that have not been invented yet in order to solve problems that are not even problems yetrdquo
ldquoShift Happensrdquo httpshifthappenswikispacescom
23
Learning analytics for this
Learning analytics for this
ldquoWhile employers continue to demand high academic standards they also now want more They want people who can adapt see connections innovate communicate and work with others This is true in many areas of work The new knowledge-based economies in particular will increasingly depend on these abilities Many businesses are paying for courses to promote creative abilities to teach the skills and attitudes that are now essential for economic successhelliprdquo
All our Futures Creativity culture amp education May 1999 24
Learning analytics for this
25
Creativity Culture and Education (2009) Changing Young Lives 2012 Newcastle CCE httpwwwcreativitycultureeducationorgchanging-young-lives-2012
Think about the analytics products and initiatives reviewed above ndash where would you locate them on these dimensions
Expert-led enquiry
Student-led enquiry
Expert-led teaching
Student-led revision
Kno
wle
dge
co
-gen
erat
ion
an
d us
e
Pre-
scrib
ed
Kno
wle
dge
Teacher agency Student agency
Repetition Abstraction Acquisition
Authenticity Agency Identity
Teaching as learning design
The Knowledge-Agency Window
Learning analytics for this
Ruth Deakin Crick Univ Bristol Centre for Systems Learning amp Leadership ldquoPedagogy of Hoperdquo httplearningemergencenet20120921pedagogy-of-hope
analytics grounded in the principles of good
assessment for learning
(not summative assessment for
grading pupils teachers institutions or nations)
27
Assessment for Learning
28
httpassessment-reform-grouporg Few learning analytics are
currently able to take o board the richness of this
original conception of assessment for learning
Assessment for Learning
29
httpassessment-reform-grouporg
Assessment for Learning
30
httpassessment-reform-grouporg
To what extent could automated
feedback be designed and evaluated with
emotional impact in mind
Assessment for Learning
31
httpassessment-reform-grouporg
Can analytics identify proxies
for such advanced qualities
Assessment for Learning
32
httpassessment-reform-grouporg
How do we provide analytics feedback
that does not disempower and de-motivate struggling
learners
analytics for learning conversations
33
Socio-cultural discourse analysis (Mercer et al OU)
bull Disputational talk characterised by disagreement and individualised decision making
bull Cumulative talk in which speakers build positively but uncritically on what the others have said
bull Exploratory talk in which partners engage critically but constructively with each others ideas
34 Mercer N (2004) Sociocultural discourse analysis analysing classroom talk as a social mode of thinking Journal of Applied Linguistics 1(2) 137-168
bull Exploratory talk in which partners engage critically but constructively with each others ideas
bull Statements and suggestions are offered for joint consideration
bull These may be challenged and counter-challenged but challenges are justified and alternative hypotheses are offered
bull Partners all actively participate and opinions are sought and considered before decisions are jointly made
bull Compared with the other two types in Exploratory talk knowledge is made more publicly accountable and reasoning is more visible in the talk
35
Socio-cultural discourse analysis (Mercer et al OU)
Mercer N (2004) Sociocultural discourse analysis analysing classroom talk as a social mode of thinking Journal of Applied Linguistics 1(2) 137-168
Analytics for identifying Exploratory talk
36
Elluminate sessions can be very long ndash lasting for hours or even covering days of a conference
It would be useful if we could identify where quality learning conversations seem to be taking place so we can recommend those sessions and not have to sit through online chat about virtual biscuits
Ferguson R and Buckingham Shum S Learning analytics to identify exploratory dialogue within synchronous text chat 1st International Conference on Learning Analytics amp Knowledge (Banff Canada 27 Mar-1 Apr 2011)
Defining indicators of Exploratory Talk
37
Category Indicator Challenge But if have to respond my view Critique However Irsquom not sure maybe Discussion of resources
Have you read more links
Evaluation Good example good point Explanation Means that our goals Explicit reasoning Next step relates to thatrsquos why Justification I mean we learned we observed Reflections of perspectives of others
Agree here is another makes the point take your point your view
Extract classified as Exploratory Talk
38
Time Contribution 242 PM I hate talking -P My question was whether gadgets were just
basically widgets and we could embed them in various web sites like Netvibes Google Desktop etc
242 PM Thanks thats great I am sure I understood everything but looks inspiring
243 PM Yes why OU tools not generic tools
243 PM Issues of interoperability
243 PM The new SocialLearn site looks a lot like a corkboard where you can add various widgets similar to those existing web start pages
243 PM What if we end up with as many appsgadgets as we have social networks and then we need a recommender for the apps
243 PM My question was on the definition of the crowd in the wisdom of crowds we acsess in the service model
243 PM there are various different flavours of widget eg Google gadgets W3C widgets etc SocialLearn has gone for Google gadgets
-60
-40
-20
0
20
40
60
80
92
8 9
32
93
6
94
0
94
1
94
6
95
0
95
3
95
6
10
00
10
05
10
07
10
07
10
09
10
13
10
17
10
23
10
27
10
31
10
35
10
40
10
45
10
52
10
55
11
04
11
08
11
11
11
17
11
20
11
24
11
26
11
28
11
31
11
32
11
35
11
36
11
38
11
39
11
41
11
44
11
46
11
48
11
52
11
54
12
00
12
03
12
04
12
05
Average Exploratory
Discourse analytics on webinar textchat
Sheffield UK not as sunny as yesterday - still warm Greetings from Hong Kong Morning from Wiltshire sunny here
See you bye for now bye and thank you Bye all for now
Extensions to Ferguson R and Buckingham Shum S (2011) Learning Analytics to Identify Exploratory Dialogue within Synchronous Text Chat Proc 1st Int Conf Learning Analytics amp Knowledge Feb 27-Mar 1 2011 Banff ACM Press Eprint httporoopenacuk28955
Given a 25 hour webinar where in the live textchat were the most effective learning conversations Not at the start and end of a webinar but if we zoom in on a peakhellip
Discourse analytics on webinar textchat
-100
-50
0
50
100
92
8 9
40
9
50
1
000
1
007
1
017
1
031
1
045
1
104
1
117
1
126
1
132
1
138
1
144
1
152
1
203
Averag
Wei amp He extensions to Ferguson R and Buckingham Shum S (2011) Learning Analytics to Identify Exploratory Dialogue within Synchronous Text Chat Proc 1st Int Conf Learning Analytics amp Knowledge Feb 27-Mar 1 2011 Banff ACM Press Eprint httporoopenacuk28955
Classified as ldquoexploratory
talkrdquo
(more substantive for learning)
ldquonon-exploratoryrdquo
Given a 25 hour webinar where in the live textchat were the most effective learning conversations
De Liddo A Buckingham Shum S Quinto I Bachler M and Cannavacciuolo L Discourse-centric learning analytics 1st International Conference on Learning Analytics amp Knowledge (Banff 27 Mar-1 Apr 2011) httporoopenacuk25829
KMirsquos Cohere a web deliberation platform enabling semantic social network and discourse network analytics
Rebecca is playing the role of broker
connecting 2 peersrsquo contributions in meaningful ways
analytics for scholarly writing
42
Discourse analysis (Xerox Incremental Parser)
BACKGROUND KNOWLEDGE
Recent studies indicate hellip
hellip the previously proposed hellip
hellip is universally accepted
NOVELTY
new insights provide direct evidence
we suggest a new approach
results define a novel role
OPEN QUESTION hellip little is known hellip hellip role hellip has been elusive
Current data is insufficient hellip
GENERALIZING emerging as a promising approach Our understanding has grown exponentially growing recognition of the
importance
CONRASTING IDEAS hellip unorthodox view resolves hellip paradoxes hellip
In contrast with previous hypotheses
inconsistent with past findings
SIGNIFICANCE studies have provided important advances
Knowledge is crucial for understanding
valuable information from studies
SURPRISE We have recently observed surprisingly
We have identified unusual The recent discovery suggests intriguing roles
SUMMARIZING The goal of this study Here we show
Altogether our results indicate
Detection of salient sentences in scholarly reports based on the rhetorical signals authors use
Aacutegnes Saacutendor amp OLnet Project httpolnetorgnode512
De Liddo A Saacutendor Aacute and Buckingham Shum S Contested Collective Intelligence Rationale Technologies and a Human-Machine Annotation Study Computer Supported Cooperative Work 21 4-5 (2012) 417-448 httporoopenacuk31052
Human and machine analysis of a text for key contributions
19 sentences annotated 22 sentences annotated 11 sentences same as human annotation
71 sentences annotated 59 sentences annotated 42 sentences same as human annotation
Document 1
Document 2
httptechnologieskmiopenacukcohere20120109cohere-plus-automated-rhetorical-annotation De Liddo A Saacutendor Aacute and Buckingham Shum S Contested Collective Intelligence Rationale Technologies and a Human-Machine Annotation Study Computer Supported Cooperative Work 21 4-5 (2012) 417-448 httporoopenacuk31052
analytics for intepersonal networking
45
Semantic Social Network Analytics
De Liddo A Buckingham Shum S Quinto I Bachler M and Cannavacciuolo L Discourse-centric learning analytics 1st International Conference on Learning Analytics amp Knowledge (Banff 27 Mar-1 Apr 2011) httporoopenacuk25829
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Closing thoughts
54
55
ldquoThe basic question is not what can we measure
The basic question is
what does a good education look likerdquo
(Gardner Campbell)
httpchroniclecomblogstechtherapy20120502episode-95-learning-analytics-could-lead-to-wal-martification-of-college httplak12wikispacescomRecordings
56
Our analytics promote values pedagogy and assessment regimes
Are we clear which master
our analytics serve Are we happy to be judged by them
57
Will learning analytics merely turbocharge the current educational paradigm
mdash which is so often declared
not fit for purposehellip
58
hellipor will learning analytics reflect what we now know about designing authentic
engaged learning developing the new qualities that a
complex society demands
Where did the data come from
17
learners
researchers educators instructional designers
theories pedagogies
assessments tools
Where did the data come from
18
learners
researchers educators instructional designers
theories pedagogies
assessments tools
technologists
Data Intent
The map is not the territory Analytics are not the end but a means The goal is to optimize the whole system
19
learners
researchers educators instructional designers
theories pedagogies
assessments tools
desi
gn feedback
intent
outcome
Optimize the system for what
20
Same outcomes but higher scores
Learning Analytics as
Evolutionary Technology
bull more engaging bull better assessed bull better outcomes
bull deliverable at scale
21
New outcomes we couldnrsquot assess before
Learning Analytics as
Revolutionary Technology
bull learner behaviours quantifiable bull interpersonal networks quantifiable
bull discourse quantifiable bull moods and dispositions quantifiable
22
ldquoWe are preparing students for jobs that do not exist yet that will use technologies that have not been invented yet in order to solve problems that are not even problems yetrdquo
ldquoShift Happensrdquo httpshifthappenswikispacescom
23
Learning analytics for this
Learning analytics for this
ldquoWhile employers continue to demand high academic standards they also now want more They want people who can adapt see connections innovate communicate and work with others This is true in many areas of work The new knowledge-based economies in particular will increasingly depend on these abilities Many businesses are paying for courses to promote creative abilities to teach the skills and attitudes that are now essential for economic successhelliprdquo
All our Futures Creativity culture amp education May 1999 24
Learning analytics for this
25
Creativity Culture and Education (2009) Changing Young Lives 2012 Newcastle CCE httpwwwcreativitycultureeducationorgchanging-young-lives-2012
Think about the analytics products and initiatives reviewed above ndash where would you locate them on these dimensions
Expert-led enquiry
Student-led enquiry
Expert-led teaching
Student-led revision
Kno
wle
dge
co
-gen
erat
ion
an
d us
e
Pre-
scrib
ed
Kno
wle
dge
Teacher agency Student agency
Repetition Abstraction Acquisition
Authenticity Agency Identity
Teaching as learning design
The Knowledge-Agency Window
Learning analytics for this
Ruth Deakin Crick Univ Bristol Centre for Systems Learning amp Leadership ldquoPedagogy of Hoperdquo httplearningemergencenet20120921pedagogy-of-hope
analytics grounded in the principles of good
assessment for learning
(not summative assessment for
grading pupils teachers institutions or nations)
27
Assessment for Learning
28
httpassessment-reform-grouporg Few learning analytics are
currently able to take o board the richness of this
original conception of assessment for learning
Assessment for Learning
29
httpassessment-reform-grouporg
Assessment for Learning
30
httpassessment-reform-grouporg
To what extent could automated
feedback be designed and evaluated with
emotional impact in mind
Assessment for Learning
31
httpassessment-reform-grouporg
Can analytics identify proxies
for such advanced qualities
Assessment for Learning
32
httpassessment-reform-grouporg
How do we provide analytics feedback
that does not disempower and de-motivate struggling
learners
analytics for learning conversations
33
Socio-cultural discourse analysis (Mercer et al OU)
bull Disputational talk characterised by disagreement and individualised decision making
bull Cumulative talk in which speakers build positively but uncritically on what the others have said
bull Exploratory talk in which partners engage critically but constructively with each others ideas
34 Mercer N (2004) Sociocultural discourse analysis analysing classroom talk as a social mode of thinking Journal of Applied Linguistics 1(2) 137-168
bull Exploratory talk in which partners engage critically but constructively with each others ideas
bull Statements and suggestions are offered for joint consideration
bull These may be challenged and counter-challenged but challenges are justified and alternative hypotheses are offered
bull Partners all actively participate and opinions are sought and considered before decisions are jointly made
bull Compared with the other two types in Exploratory talk knowledge is made more publicly accountable and reasoning is more visible in the talk
35
Socio-cultural discourse analysis (Mercer et al OU)
Mercer N (2004) Sociocultural discourse analysis analysing classroom talk as a social mode of thinking Journal of Applied Linguistics 1(2) 137-168
Analytics for identifying Exploratory talk
36
Elluminate sessions can be very long ndash lasting for hours or even covering days of a conference
It would be useful if we could identify where quality learning conversations seem to be taking place so we can recommend those sessions and not have to sit through online chat about virtual biscuits
Ferguson R and Buckingham Shum S Learning analytics to identify exploratory dialogue within synchronous text chat 1st International Conference on Learning Analytics amp Knowledge (Banff Canada 27 Mar-1 Apr 2011)
Defining indicators of Exploratory Talk
37
Category Indicator Challenge But if have to respond my view Critique However Irsquom not sure maybe Discussion of resources
Have you read more links
Evaluation Good example good point Explanation Means that our goals Explicit reasoning Next step relates to thatrsquos why Justification I mean we learned we observed Reflections of perspectives of others
Agree here is another makes the point take your point your view
Extract classified as Exploratory Talk
38
Time Contribution 242 PM I hate talking -P My question was whether gadgets were just
basically widgets and we could embed them in various web sites like Netvibes Google Desktop etc
242 PM Thanks thats great I am sure I understood everything but looks inspiring
243 PM Yes why OU tools not generic tools
243 PM Issues of interoperability
243 PM The new SocialLearn site looks a lot like a corkboard where you can add various widgets similar to those existing web start pages
243 PM What if we end up with as many appsgadgets as we have social networks and then we need a recommender for the apps
243 PM My question was on the definition of the crowd in the wisdom of crowds we acsess in the service model
243 PM there are various different flavours of widget eg Google gadgets W3C widgets etc SocialLearn has gone for Google gadgets
-60
-40
-20
0
20
40
60
80
92
8 9
32
93
6
94
0
94
1
94
6
95
0
95
3
95
6
10
00
10
05
10
07
10
07
10
09
10
13
10
17
10
23
10
27
10
31
10
35
10
40
10
45
10
52
10
55
11
04
11
08
11
11
11
17
11
20
11
24
11
26
11
28
11
31
11
32
11
35
11
36
11
38
11
39
11
41
11
44
11
46
11
48
11
52
11
54
12
00
12
03
12
04
12
05
Average Exploratory
Discourse analytics on webinar textchat
Sheffield UK not as sunny as yesterday - still warm Greetings from Hong Kong Morning from Wiltshire sunny here
See you bye for now bye and thank you Bye all for now
Extensions to Ferguson R and Buckingham Shum S (2011) Learning Analytics to Identify Exploratory Dialogue within Synchronous Text Chat Proc 1st Int Conf Learning Analytics amp Knowledge Feb 27-Mar 1 2011 Banff ACM Press Eprint httporoopenacuk28955
Given a 25 hour webinar where in the live textchat were the most effective learning conversations Not at the start and end of a webinar but if we zoom in on a peakhellip
Discourse analytics on webinar textchat
-100
-50
0
50
100
92
8 9
40
9
50
1
000
1
007
1
017
1
031
1
045
1
104
1
117
1
126
1
132
1
138
1
144
1
152
1
203
Averag
Wei amp He extensions to Ferguson R and Buckingham Shum S (2011) Learning Analytics to Identify Exploratory Dialogue within Synchronous Text Chat Proc 1st Int Conf Learning Analytics amp Knowledge Feb 27-Mar 1 2011 Banff ACM Press Eprint httporoopenacuk28955
Classified as ldquoexploratory
talkrdquo
(more substantive for learning)
ldquonon-exploratoryrdquo
Given a 25 hour webinar where in the live textchat were the most effective learning conversations
De Liddo A Buckingham Shum S Quinto I Bachler M and Cannavacciuolo L Discourse-centric learning analytics 1st International Conference on Learning Analytics amp Knowledge (Banff 27 Mar-1 Apr 2011) httporoopenacuk25829
KMirsquos Cohere a web deliberation platform enabling semantic social network and discourse network analytics
Rebecca is playing the role of broker
connecting 2 peersrsquo contributions in meaningful ways
analytics for scholarly writing
42
Discourse analysis (Xerox Incremental Parser)
BACKGROUND KNOWLEDGE
Recent studies indicate hellip
hellip the previously proposed hellip
hellip is universally accepted
NOVELTY
new insights provide direct evidence
we suggest a new approach
results define a novel role
OPEN QUESTION hellip little is known hellip hellip role hellip has been elusive
Current data is insufficient hellip
GENERALIZING emerging as a promising approach Our understanding has grown exponentially growing recognition of the
importance
CONRASTING IDEAS hellip unorthodox view resolves hellip paradoxes hellip
In contrast with previous hypotheses
inconsistent with past findings
SIGNIFICANCE studies have provided important advances
Knowledge is crucial for understanding
valuable information from studies
SURPRISE We have recently observed surprisingly
We have identified unusual The recent discovery suggests intriguing roles
SUMMARIZING The goal of this study Here we show
Altogether our results indicate
Detection of salient sentences in scholarly reports based on the rhetorical signals authors use
Aacutegnes Saacutendor amp OLnet Project httpolnetorgnode512
De Liddo A Saacutendor Aacute and Buckingham Shum S Contested Collective Intelligence Rationale Technologies and a Human-Machine Annotation Study Computer Supported Cooperative Work 21 4-5 (2012) 417-448 httporoopenacuk31052
Human and machine analysis of a text for key contributions
19 sentences annotated 22 sentences annotated 11 sentences same as human annotation
71 sentences annotated 59 sentences annotated 42 sentences same as human annotation
Document 1
Document 2
httptechnologieskmiopenacukcohere20120109cohere-plus-automated-rhetorical-annotation De Liddo A Saacutendor Aacute and Buckingham Shum S Contested Collective Intelligence Rationale Technologies and a Human-Machine Annotation Study Computer Supported Cooperative Work 21 4-5 (2012) 417-448 httporoopenacuk31052
analytics for intepersonal networking
45
Semantic Social Network Analytics
De Liddo A Buckingham Shum S Quinto I Bachler M and Cannavacciuolo L Discourse-centric learning analytics 1st International Conference on Learning Analytics amp Knowledge (Banff 27 Mar-1 Apr 2011) httporoopenacuk25829
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Closing thoughts
54
55
ldquoThe basic question is not what can we measure
The basic question is
what does a good education look likerdquo
(Gardner Campbell)
httpchroniclecomblogstechtherapy20120502episode-95-learning-analytics-could-lead-to-wal-martification-of-college httplak12wikispacescomRecordings
56
Our analytics promote values pedagogy and assessment regimes
Are we clear which master
our analytics serve Are we happy to be judged by them
57
Will learning analytics merely turbocharge the current educational paradigm
mdash which is so often declared
not fit for purposehellip
58
hellipor will learning analytics reflect what we now know about designing authentic
engaged learning developing the new qualities that a
complex society demands
Where did the data come from
18
learners
researchers educators instructional designers
theories pedagogies
assessments tools
technologists
Data Intent
The map is not the territory Analytics are not the end but a means The goal is to optimize the whole system
19
learners
researchers educators instructional designers
theories pedagogies
assessments tools
desi
gn feedback
intent
outcome
Optimize the system for what
20
Same outcomes but higher scores
Learning Analytics as
Evolutionary Technology
bull more engaging bull better assessed bull better outcomes
bull deliverable at scale
21
New outcomes we couldnrsquot assess before
Learning Analytics as
Revolutionary Technology
bull learner behaviours quantifiable bull interpersonal networks quantifiable
bull discourse quantifiable bull moods and dispositions quantifiable
22
ldquoWe are preparing students for jobs that do not exist yet that will use technologies that have not been invented yet in order to solve problems that are not even problems yetrdquo
ldquoShift Happensrdquo httpshifthappenswikispacescom
23
Learning analytics for this
Learning analytics for this
ldquoWhile employers continue to demand high academic standards they also now want more They want people who can adapt see connections innovate communicate and work with others This is true in many areas of work The new knowledge-based economies in particular will increasingly depend on these abilities Many businesses are paying for courses to promote creative abilities to teach the skills and attitudes that are now essential for economic successhelliprdquo
All our Futures Creativity culture amp education May 1999 24
Learning analytics for this
25
Creativity Culture and Education (2009) Changing Young Lives 2012 Newcastle CCE httpwwwcreativitycultureeducationorgchanging-young-lives-2012
Think about the analytics products and initiatives reviewed above ndash where would you locate them on these dimensions
Expert-led enquiry
Student-led enquiry
Expert-led teaching
Student-led revision
Kno
wle
dge
co
-gen
erat
ion
an
d us
e
Pre-
scrib
ed
Kno
wle
dge
Teacher agency Student agency
Repetition Abstraction Acquisition
Authenticity Agency Identity
Teaching as learning design
The Knowledge-Agency Window
Learning analytics for this
Ruth Deakin Crick Univ Bristol Centre for Systems Learning amp Leadership ldquoPedagogy of Hoperdquo httplearningemergencenet20120921pedagogy-of-hope
analytics grounded in the principles of good
assessment for learning
(not summative assessment for
grading pupils teachers institutions or nations)
27
Assessment for Learning
28
httpassessment-reform-grouporg Few learning analytics are
currently able to take o board the richness of this
original conception of assessment for learning
Assessment for Learning
29
httpassessment-reform-grouporg
Assessment for Learning
30
httpassessment-reform-grouporg
To what extent could automated
feedback be designed and evaluated with
emotional impact in mind
Assessment for Learning
31
httpassessment-reform-grouporg
Can analytics identify proxies
for such advanced qualities
Assessment for Learning
32
httpassessment-reform-grouporg
How do we provide analytics feedback
that does not disempower and de-motivate struggling
learners
analytics for learning conversations
33
Socio-cultural discourse analysis (Mercer et al OU)
bull Disputational talk characterised by disagreement and individualised decision making
bull Cumulative talk in which speakers build positively but uncritically on what the others have said
bull Exploratory talk in which partners engage critically but constructively with each others ideas
34 Mercer N (2004) Sociocultural discourse analysis analysing classroom talk as a social mode of thinking Journal of Applied Linguistics 1(2) 137-168
bull Exploratory talk in which partners engage critically but constructively with each others ideas
bull Statements and suggestions are offered for joint consideration
bull These may be challenged and counter-challenged but challenges are justified and alternative hypotheses are offered
bull Partners all actively participate and opinions are sought and considered before decisions are jointly made
bull Compared with the other two types in Exploratory talk knowledge is made more publicly accountable and reasoning is more visible in the talk
35
Socio-cultural discourse analysis (Mercer et al OU)
Mercer N (2004) Sociocultural discourse analysis analysing classroom talk as a social mode of thinking Journal of Applied Linguistics 1(2) 137-168
Analytics for identifying Exploratory talk
36
Elluminate sessions can be very long ndash lasting for hours or even covering days of a conference
It would be useful if we could identify where quality learning conversations seem to be taking place so we can recommend those sessions and not have to sit through online chat about virtual biscuits
Ferguson R and Buckingham Shum S Learning analytics to identify exploratory dialogue within synchronous text chat 1st International Conference on Learning Analytics amp Knowledge (Banff Canada 27 Mar-1 Apr 2011)
Defining indicators of Exploratory Talk
37
Category Indicator Challenge But if have to respond my view Critique However Irsquom not sure maybe Discussion of resources
Have you read more links
Evaluation Good example good point Explanation Means that our goals Explicit reasoning Next step relates to thatrsquos why Justification I mean we learned we observed Reflections of perspectives of others
Agree here is another makes the point take your point your view
Extract classified as Exploratory Talk
38
Time Contribution 242 PM I hate talking -P My question was whether gadgets were just
basically widgets and we could embed them in various web sites like Netvibes Google Desktop etc
242 PM Thanks thats great I am sure I understood everything but looks inspiring
243 PM Yes why OU tools not generic tools
243 PM Issues of interoperability
243 PM The new SocialLearn site looks a lot like a corkboard where you can add various widgets similar to those existing web start pages
243 PM What if we end up with as many appsgadgets as we have social networks and then we need a recommender for the apps
243 PM My question was on the definition of the crowd in the wisdom of crowds we acsess in the service model
243 PM there are various different flavours of widget eg Google gadgets W3C widgets etc SocialLearn has gone for Google gadgets
-60
-40
-20
0
20
40
60
80
92
8 9
32
93
6
94
0
94
1
94
6
95
0
95
3
95
6
10
00
10
05
10
07
10
07
10
09
10
13
10
17
10
23
10
27
10
31
10
35
10
40
10
45
10
52
10
55
11
04
11
08
11
11
11
17
11
20
11
24
11
26
11
28
11
31
11
32
11
35
11
36
11
38
11
39
11
41
11
44
11
46
11
48
11
52
11
54
12
00
12
03
12
04
12
05
Average Exploratory
Discourse analytics on webinar textchat
Sheffield UK not as sunny as yesterday - still warm Greetings from Hong Kong Morning from Wiltshire sunny here
See you bye for now bye and thank you Bye all for now
Extensions to Ferguson R and Buckingham Shum S (2011) Learning Analytics to Identify Exploratory Dialogue within Synchronous Text Chat Proc 1st Int Conf Learning Analytics amp Knowledge Feb 27-Mar 1 2011 Banff ACM Press Eprint httporoopenacuk28955
Given a 25 hour webinar where in the live textchat were the most effective learning conversations Not at the start and end of a webinar but if we zoom in on a peakhellip
Discourse analytics on webinar textchat
-100
-50
0
50
100
92
8 9
40
9
50
1
000
1
007
1
017
1
031
1
045
1
104
1
117
1
126
1
132
1
138
1
144
1
152
1
203
Averag
Wei amp He extensions to Ferguson R and Buckingham Shum S (2011) Learning Analytics to Identify Exploratory Dialogue within Synchronous Text Chat Proc 1st Int Conf Learning Analytics amp Knowledge Feb 27-Mar 1 2011 Banff ACM Press Eprint httporoopenacuk28955
Classified as ldquoexploratory
talkrdquo
(more substantive for learning)
ldquonon-exploratoryrdquo
Given a 25 hour webinar where in the live textchat were the most effective learning conversations
De Liddo A Buckingham Shum S Quinto I Bachler M and Cannavacciuolo L Discourse-centric learning analytics 1st International Conference on Learning Analytics amp Knowledge (Banff 27 Mar-1 Apr 2011) httporoopenacuk25829
KMirsquos Cohere a web deliberation platform enabling semantic social network and discourse network analytics
Rebecca is playing the role of broker
connecting 2 peersrsquo contributions in meaningful ways
analytics for scholarly writing
42
Discourse analysis (Xerox Incremental Parser)
BACKGROUND KNOWLEDGE
Recent studies indicate hellip
hellip the previously proposed hellip
hellip is universally accepted
NOVELTY
new insights provide direct evidence
we suggest a new approach
results define a novel role
OPEN QUESTION hellip little is known hellip hellip role hellip has been elusive
Current data is insufficient hellip
GENERALIZING emerging as a promising approach Our understanding has grown exponentially growing recognition of the
importance
CONRASTING IDEAS hellip unorthodox view resolves hellip paradoxes hellip
In contrast with previous hypotheses
inconsistent with past findings
SIGNIFICANCE studies have provided important advances
Knowledge is crucial for understanding
valuable information from studies
SURPRISE We have recently observed surprisingly
We have identified unusual The recent discovery suggests intriguing roles
SUMMARIZING The goal of this study Here we show
Altogether our results indicate
Detection of salient sentences in scholarly reports based on the rhetorical signals authors use
Aacutegnes Saacutendor amp OLnet Project httpolnetorgnode512
De Liddo A Saacutendor Aacute and Buckingham Shum S Contested Collective Intelligence Rationale Technologies and a Human-Machine Annotation Study Computer Supported Cooperative Work 21 4-5 (2012) 417-448 httporoopenacuk31052
Human and machine analysis of a text for key contributions
19 sentences annotated 22 sentences annotated 11 sentences same as human annotation
71 sentences annotated 59 sentences annotated 42 sentences same as human annotation
Document 1
Document 2
httptechnologieskmiopenacukcohere20120109cohere-plus-automated-rhetorical-annotation De Liddo A Saacutendor Aacute and Buckingham Shum S Contested Collective Intelligence Rationale Technologies and a Human-Machine Annotation Study Computer Supported Cooperative Work 21 4-5 (2012) 417-448 httporoopenacuk31052
analytics for intepersonal networking
45
Semantic Social Network Analytics
De Liddo A Buckingham Shum S Quinto I Bachler M and Cannavacciuolo L Discourse-centric learning analytics 1st International Conference on Learning Analytics amp Knowledge (Banff 27 Mar-1 Apr 2011) httporoopenacuk25829
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Closing thoughts
54
55
ldquoThe basic question is not what can we measure
The basic question is
what does a good education look likerdquo
(Gardner Campbell)
httpchroniclecomblogstechtherapy20120502episode-95-learning-analytics-could-lead-to-wal-martification-of-college httplak12wikispacescomRecordings
56
Our analytics promote values pedagogy and assessment regimes
Are we clear which master
our analytics serve Are we happy to be judged by them
57
Will learning analytics merely turbocharge the current educational paradigm
mdash which is so often declared
not fit for purposehellip
58
hellipor will learning analytics reflect what we now know about designing authentic
engaged learning developing the new qualities that a
complex society demands
Data Intent
The map is not the territory Analytics are not the end but a means The goal is to optimize the whole system
19
learners
researchers educators instructional designers
theories pedagogies
assessments tools
desi
gn feedback
intent
outcome
Optimize the system for what
20
Same outcomes but higher scores
Learning Analytics as
Evolutionary Technology
bull more engaging bull better assessed bull better outcomes
bull deliverable at scale
21
New outcomes we couldnrsquot assess before
Learning Analytics as
Revolutionary Technology
bull learner behaviours quantifiable bull interpersonal networks quantifiable
bull discourse quantifiable bull moods and dispositions quantifiable
22
ldquoWe are preparing students for jobs that do not exist yet that will use technologies that have not been invented yet in order to solve problems that are not even problems yetrdquo
ldquoShift Happensrdquo httpshifthappenswikispacescom
23
Learning analytics for this
Learning analytics for this
ldquoWhile employers continue to demand high academic standards they also now want more They want people who can adapt see connections innovate communicate and work with others This is true in many areas of work The new knowledge-based economies in particular will increasingly depend on these abilities Many businesses are paying for courses to promote creative abilities to teach the skills and attitudes that are now essential for economic successhelliprdquo
All our Futures Creativity culture amp education May 1999 24
Learning analytics for this
25
Creativity Culture and Education (2009) Changing Young Lives 2012 Newcastle CCE httpwwwcreativitycultureeducationorgchanging-young-lives-2012
Think about the analytics products and initiatives reviewed above ndash where would you locate them on these dimensions
Expert-led enquiry
Student-led enquiry
Expert-led teaching
Student-led revision
Kno
wle
dge
co
-gen
erat
ion
an
d us
e
Pre-
scrib
ed
Kno
wle
dge
Teacher agency Student agency
Repetition Abstraction Acquisition
Authenticity Agency Identity
Teaching as learning design
The Knowledge-Agency Window
Learning analytics for this
Ruth Deakin Crick Univ Bristol Centre for Systems Learning amp Leadership ldquoPedagogy of Hoperdquo httplearningemergencenet20120921pedagogy-of-hope
analytics grounded in the principles of good
assessment for learning
(not summative assessment for
grading pupils teachers institutions or nations)
27
Assessment for Learning
28
httpassessment-reform-grouporg Few learning analytics are
currently able to take o board the richness of this
original conception of assessment for learning
Assessment for Learning
29
httpassessment-reform-grouporg
Assessment for Learning
30
httpassessment-reform-grouporg
To what extent could automated
feedback be designed and evaluated with
emotional impact in mind
Assessment for Learning
31
httpassessment-reform-grouporg
Can analytics identify proxies
for such advanced qualities
Assessment for Learning
32
httpassessment-reform-grouporg
How do we provide analytics feedback
that does not disempower and de-motivate struggling
learners
analytics for learning conversations
33
Socio-cultural discourse analysis (Mercer et al OU)
bull Disputational talk characterised by disagreement and individualised decision making
bull Cumulative talk in which speakers build positively but uncritically on what the others have said
bull Exploratory talk in which partners engage critically but constructively with each others ideas
34 Mercer N (2004) Sociocultural discourse analysis analysing classroom talk as a social mode of thinking Journal of Applied Linguistics 1(2) 137-168
bull Exploratory talk in which partners engage critically but constructively with each others ideas
bull Statements and suggestions are offered for joint consideration
bull These may be challenged and counter-challenged but challenges are justified and alternative hypotheses are offered
bull Partners all actively participate and opinions are sought and considered before decisions are jointly made
bull Compared with the other two types in Exploratory talk knowledge is made more publicly accountable and reasoning is more visible in the talk
35
Socio-cultural discourse analysis (Mercer et al OU)
Mercer N (2004) Sociocultural discourse analysis analysing classroom talk as a social mode of thinking Journal of Applied Linguistics 1(2) 137-168
Analytics for identifying Exploratory talk
36
Elluminate sessions can be very long ndash lasting for hours or even covering days of a conference
It would be useful if we could identify where quality learning conversations seem to be taking place so we can recommend those sessions and not have to sit through online chat about virtual biscuits
Ferguson R and Buckingham Shum S Learning analytics to identify exploratory dialogue within synchronous text chat 1st International Conference on Learning Analytics amp Knowledge (Banff Canada 27 Mar-1 Apr 2011)
Defining indicators of Exploratory Talk
37
Category Indicator Challenge But if have to respond my view Critique However Irsquom not sure maybe Discussion of resources
Have you read more links
Evaluation Good example good point Explanation Means that our goals Explicit reasoning Next step relates to thatrsquos why Justification I mean we learned we observed Reflections of perspectives of others
Agree here is another makes the point take your point your view
Extract classified as Exploratory Talk
38
Time Contribution 242 PM I hate talking -P My question was whether gadgets were just
basically widgets and we could embed them in various web sites like Netvibes Google Desktop etc
242 PM Thanks thats great I am sure I understood everything but looks inspiring
243 PM Yes why OU tools not generic tools
243 PM Issues of interoperability
243 PM The new SocialLearn site looks a lot like a corkboard where you can add various widgets similar to those existing web start pages
243 PM What if we end up with as many appsgadgets as we have social networks and then we need a recommender for the apps
243 PM My question was on the definition of the crowd in the wisdom of crowds we acsess in the service model
243 PM there are various different flavours of widget eg Google gadgets W3C widgets etc SocialLearn has gone for Google gadgets
-60
-40
-20
0
20
40
60
80
92
8 9
32
93
6
94
0
94
1
94
6
95
0
95
3
95
6
10
00
10
05
10
07
10
07
10
09
10
13
10
17
10
23
10
27
10
31
10
35
10
40
10
45
10
52
10
55
11
04
11
08
11
11
11
17
11
20
11
24
11
26
11
28
11
31
11
32
11
35
11
36
11
38
11
39
11
41
11
44
11
46
11
48
11
52
11
54
12
00
12
03
12
04
12
05
Average Exploratory
Discourse analytics on webinar textchat
Sheffield UK not as sunny as yesterday - still warm Greetings from Hong Kong Morning from Wiltshire sunny here
See you bye for now bye and thank you Bye all for now
Extensions to Ferguson R and Buckingham Shum S (2011) Learning Analytics to Identify Exploratory Dialogue within Synchronous Text Chat Proc 1st Int Conf Learning Analytics amp Knowledge Feb 27-Mar 1 2011 Banff ACM Press Eprint httporoopenacuk28955
Given a 25 hour webinar where in the live textchat were the most effective learning conversations Not at the start and end of a webinar but if we zoom in on a peakhellip
Discourse analytics on webinar textchat
-100
-50
0
50
100
92
8 9
40
9
50
1
000
1
007
1
017
1
031
1
045
1
104
1
117
1
126
1
132
1
138
1
144
1
152
1
203
Averag
Wei amp He extensions to Ferguson R and Buckingham Shum S (2011) Learning Analytics to Identify Exploratory Dialogue within Synchronous Text Chat Proc 1st Int Conf Learning Analytics amp Knowledge Feb 27-Mar 1 2011 Banff ACM Press Eprint httporoopenacuk28955
Classified as ldquoexploratory
talkrdquo
(more substantive for learning)
ldquonon-exploratoryrdquo
Given a 25 hour webinar where in the live textchat were the most effective learning conversations
De Liddo A Buckingham Shum S Quinto I Bachler M and Cannavacciuolo L Discourse-centric learning analytics 1st International Conference on Learning Analytics amp Knowledge (Banff 27 Mar-1 Apr 2011) httporoopenacuk25829
KMirsquos Cohere a web deliberation platform enabling semantic social network and discourse network analytics
Rebecca is playing the role of broker
connecting 2 peersrsquo contributions in meaningful ways
analytics for scholarly writing
42
Discourse analysis (Xerox Incremental Parser)
BACKGROUND KNOWLEDGE
Recent studies indicate hellip
hellip the previously proposed hellip
hellip is universally accepted
NOVELTY
new insights provide direct evidence
we suggest a new approach
results define a novel role
OPEN QUESTION hellip little is known hellip hellip role hellip has been elusive
Current data is insufficient hellip
GENERALIZING emerging as a promising approach Our understanding has grown exponentially growing recognition of the
importance
CONRASTING IDEAS hellip unorthodox view resolves hellip paradoxes hellip
In contrast with previous hypotheses
inconsistent with past findings
SIGNIFICANCE studies have provided important advances
Knowledge is crucial for understanding
valuable information from studies
SURPRISE We have recently observed surprisingly
We have identified unusual The recent discovery suggests intriguing roles
SUMMARIZING The goal of this study Here we show
Altogether our results indicate
Detection of salient sentences in scholarly reports based on the rhetorical signals authors use
Aacutegnes Saacutendor amp OLnet Project httpolnetorgnode512
De Liddo A Saacutendor Aacute and Buckingham Shum S Contested Collective Intelligence Rationale Technologies and a Human-Machine Annotation Study Computer Supported Cooperative Work 21 4-5 (2012) 417-448 httporoopenacuk31052
Human and machine analysis of a text for key contributions
19 sentences annotated 22 sentences annotated 11 sentences same as human annotation
71 sentences annotated 59 sentences annotated 42 sentences same as human annotation
Document 1
Document 2
httptechnologieskmiopenacukcohere20120109cohere-plus-automated-rhetorical-annotation De Liddo A Saacutendor Aacute and Buckingham Shum S Contested Collective Intelligence Rationale Technologies and a Human-Machine Annotation Study Computer Supported Cooperative Work 21 4-5 (2012) 417-448 httporoopenacuk31052
analytics for intepersonal networking
45
Semantic Social Network Analytics
De Liddo A Buckingham Shum S Quinto I Bachler M and Cannavacciuolo L Discourse-centric learning analytics 1st International Conference on Learning Analytics amp Knowledge (Banff 27 Mar-1 Apr 2011) httporoopenacuk25829
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Closing thoughts
54
55
ldquoThe basic question is not what can we measure
The basic question is
what does a good education look likerdquo
(Gardner Campbell)
httpchroniclecomblogstechtherapy20120502episode-95-learning-analytics-could-lead-to-wal-martification-of-college httplak12wikispacescomRecordings
56
Our analytics promote values pedagogy and assessment regimes
Are we clear which master
our analytics serve Are we happy to be judged by them
57
Will learning analytics merely turbocharge the current educational paradigm
mdash which is so often declared
not fit for purposehellip
58
hellipor will learning analytics reflect what we now know about designing authentic
engaged learning developing the new qualities that a
complex society demands
Optimize the system for what
20
Same outcomes but higher scores
Learning Analytics as
Evolutionary Technology
bull more engaging bull better assessed bull better outcomes
bull deliverable at scale
21
New outcomes we couldnrsquot assess before
Learning Analytics as
Revolutionary Technology
bull learner behaviours quantifiable bull interpersonal networks quantifiable
bull discourse quantifiable bull moods and dispositions quantifiable
22
ldquoWe are preparing students for jobs that do not exist yet that will use technologies that have not been invented yet in order to solve problems that are not even problems yetrdquo
ldquoShift Happensrdquo httpshifthappenswikispacescom
23
Learning analytics for this
Learning analytics for this
ldquoWhile employers continue to demand high academic standards they also now want more They want people who can adapt see connections innovate communicate and work with others This is true in many areas of work The new knowledge-based economies in particular will increasingly depend on these abilities Many businesses are paying for courses to promote creative abilities to teach the skills and attitudes that are now essential for economic successhelliprdquo
All our Futures Creativity culture amp education May 1999 24
Learning analytics for this
25
Creativity Culture and Education (2009) Changing Young Lives 2012 Newcastle CCE httpwwwcreativitycultureeducationorgchanging-young-lives-2012
Think about the analytics products and initiatives reviewed above ndash where would you locate them on these dimensions
Expert-led enquiry
Student-led enquiry
Expert-led teaching
Student-led revision
Kno
wle
dge
co
-gen
erat
ion
an
d us
e
Pre-
scrib
ed
Kno
wle
dge
Teacher agency Student agency
Repetition Abstraction Acquisition
Authenticity Agency Identity
Teaching as learning design
The Knowledge-Agency Window
Learning analytics for this
Ruth Deakin Crick Univ Bristol Centre for Systems Learning amp Leadership ldquoPedagogy of Hoperdquo httplearningemergencenet20120921pedagogy-of-hope
analytics grounded in the principles of good
assessment for learning
(not summative assessment for
grading pupils teachers institutions or nations)
27
Assessment for Learning
28
httpassessment-reform-grouporg Few learning analytics are
currently able to take o board the richness of this
original conception of assessment for learning
Assessment for Learning
29
httpassessment-reform-grouporg
Assessment for Learning
30
httpassessment-reform-grouporg
To what extent could automated
feedback be designed and evaluated with
emotional impact in mind
Assessment for Learning
31
httpassessment-reform-grouporg
Can analytics identify proxies
for such advanced qualities
Assessment for Learning
32
httpassessment-reform-grouporg
How do we provide analytics feedback
that does not disempower and de-motivate struggling
learners
analytics for learning conversations
33
Socio-cultural discourse analysis (Mercer et al OU)
bull Disputational talk characterised by disagreement and individualised decision making
bull Cumulative talk in which speakers build positively but uncritically on what the others have said
bull Exploratory talk in which partners engage critically but constructively with each others ideas
34 Mercer N (2004) Sociocultural discourse analysis analysing classroom talk as a social mode of thinking Journal of Applied Linguistics 1(2) 137-168
bull Exploratory talk in which partners engage critically but constructively with each others ideas
bull Statements and suggestions are offered for joint consideration
bull These may be challenged and counter-challenged but challenges are justified and alternative hypotheses are offered
bull Partners all actively participate and opinions are sought and considered before decisions are jointly made
bull Compared with the other two types in Exploratory talk knowledge is made more publicly accountable and reasoning is more visible in the talk
35
Socio-cultural discourse analysis (Mercer et al OU)
Mercer N (2004) Sociocultural discourse analysis analysing classroom talk as a social mode of thinking Journal of Applied Linguistics 1(2) 137-168
Analytics for identifying Exploratory talk
36
Elluminate sessions can be very long ndash lasting for hours or even covering days of a conference
It would be useful if we could identify where quality learning conversations seem to be taking place so we can recommend those sessions and not have to sit through online chat about virtual biscuits
Ferguson R and Buckingham Shum S Learning analytics to identify exploratory dialogue within synchronous text chat 1st International Conference on Learning Analytics amp Knowledge (Banff Canada 27 Mar-1 Apr 2011)
Defining indicators of Exploratory Talk
37
Category Indicator Challenge But if have to respond my view Critique However Irsquom not sure maybe Discussion of resources
Have you read more links
Evaluation Good example good point Explanation Means that our goals Explicit reasoning Next step relates to thatrsquos why Justification I mean we learned we observed Reflections of perspectives of others
Agree here is another makes the point take your point your view
Extract classified as Exploratory Talk
38
Time Contribution 242 PM I hate talking -P My question was whether gadgets were just
basically widgets and we could embed them in various web sites like Netvibes Google Desktop etc
242 PM Thanks thats great I am sure I understood everything but looks inspiring
243 PM Yes why OU tools not generic tools
243 PM Issues of interoperability
243 PM The new SocialLearn site looks a lot like a corkboard where you can add various widgets similar to those existing web start pages
243 PM What if we end up with as many appsgadgets as we have social networks and then we need a recommender for the apps
243 PM My question was on the definition of the crowd in the wisdom of crowds we acsess in the service model
243 PM there are various different flavours of widget eg Google gadgets W3C widgets etc SocialLearn has gone for Google gadgets
-60
-40
-20
0
20
40
60
80
92
8 9
32
93
6
94
0
94
1
94
6
95
0
95
3
95
6
10
00
10
05
10
07
10
07
10
09
10
13
10
17
10
23
10
27
10
31
10
35
10
40
10
45
10
52
10
55
11
04
11
08
11
11
11
17
11
20
11
24
11
26
11
28
11
31
11
32
11
35
11
36
11
38
11
39
11
41
11
44
11
46
11
48
11
52
11
54
12
00
12
03
12
04
12
05
Average Exploratory
Discourse analytics on webinar textchat
Sheffield UK not as sunny as yesterday - still warm Greetings from Hong Kong Morning from Wiltshire sunny here
See you bye for now bye and thank you Bye all for now
Extensions to Ferguson R and Buckingham Shum S (2011) Learning Analytics to Identify Exploratory Dialogue within Synchronous Text Chat Proc 1st Int Conf Learning Analytics amp Knowledge Feb 27-Mar 1 2011 Banff ACM Press Eprint httporoopenacuk28955
Given a 25 hour webinar where in the live textchat were the most effective learning conversations Not at the start and end of a webinar but if we zoom in on a peakhellip
Discourse analytics on webinar textchat
-100
-50
0
50
100
92
8 9
40
9
50
1
000
1
007
1
017
1
031
1
045
1
104
1
117
1
126
1
132
1
138
1
144
1
152
1
203
Averag
Wei amp He extensions to Ferguson R and Buckingham Shum S (2011) Learning Analytics to Identify Exploratory Dialogue within Synchronous Text Chat Proc 1st Int Conf Learning Analytics amp Knowledge Feb 27-Mar 1 2011 Banff ACM Press Eprint httporoopenacuk28955
Classified as ldquoexploratory
talkrdquo
(more substantive for learning)
ldquonon-exploratoryrdquo
Given a 25 hour webinar where in the live textchat were the most effective learning conversations
De Liddo A Buckingham Shum S Quinto I Bachler M and Cannavacciuolo L Discourse-centric learning analytics 1st International Conference on Learning Analytics amp Knowledge (Banff 27 Mar-1 Apr 2011) httporoopenacuk25829
KMirsquos Cohere a web deliberation platform enabling semantic social network and discourse network analytics
Rebecca is playing the role of broker
connecting 2 peersrsquo contributions in meaningful ways
analytics for scholarly writing
42
Discourse analysis (Xerox Incremental Parser)
BACKGROUND KNOWLEDGE
Recent studies indicate hellip
hellip the previously proposed hellip
hellip is universally accepted
NOVELTY
new insights provide direct evidence
we suggest a new approach
results define a novel role
OPEN QUESTION hellip little is known hellip hellip role hellip has been elusive
Current data is insufficient hellip
GENERALIZING emerging as a promising approach Our understanding has grown exponentially growing recognition of the
importance
CONRASTING IDEAS hellip unorthodox view resolves hellip paradoxes hellip
In contrast with previous hypotheses
inconsistent with past findings
SIGNIFICANCE studies have provided important advances
Knowledge is crucial for understanding
valuable information from studies
SURPRISE We have recently observed surprisingly
We have identified unusual The recent discovery suggests intriguing roles
SUMMARIZING The goal of this study Here we show
Altogether our results indicate
Detection of salient sentences in scholarly reports based on the rhetorical signals authors use
Aacutegnes Saacutendor amp OLnet Project httpolnetorgnode512
De Liddo A Saacutendor Aacute and Buckingham Shum S Contested Collective Intelligence Rationale Technologies and a Human-Machine Annotation Study Computer Supported Cooperative Work 21 4-5 (2012) 417-448 httporoopenacuk31052
Human and machine analysis of a text for key contributions
19 sentences annotated 22 sentences annotated 11 sentences same as human annotation
71 sentences annotated 59 sentences annotated 42 sentences same as human annotation
Document 1
Document 2
httptechnologieskmiopenacukcohere20120109cohere-plus-automated-rhetorical-annotation De Liddo A Saacutendor Aacute and Buckingham Shum S Contested Collective Intelligence Rationale Technologies and a Human-Machine Annotation Study Computer Supported Cooperative Work 21 4-5 (2012) 417-448 httporoopenacuk31052
analytics for intepersonal networking
45
Semantic Social Network Analytics
De Liddo A Buckingham Shum S Quinto I Bachler M and Cannavacciuolo L Discourse-centric learning analytics 1st International Conference on Learning Analytics amp Knowledge (Banff 27 Mar-1 Apr 2011) httporoopenacuk25829
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Closing thoughts
54
55
ldquoThe basic question is not what can we measure
The basic question is
what does a good education look likerdquo
(Gardner Campbell)
httpchroniclecomblogstechtherapy20120502episode-95-learning-analytics-could-lead-to-wal-martification-of-college httplak12wikispacescomRecordings
56
Our analytics promote values pedagogy and assessment regimes
Are we clear which master
our analytics serve Are we happy to be judged by them
57
Will learning analytics merely turbocharge the current educational paradigm
mdash which is so often declared
not fit for purposehellip
58
hellipor will learning analytics reflect what we now know about designing authentic
engaged learning developing the new qualities that a
complex society demands
Same outcomes but higher scores
Learning Analytics as
Evolutionary Technology
bull more engaging bull better assessed bull better outcomes
bull deliverable at scale
21
New outcomes we couldnrsquot assess before
Learning Analytics as
Revolutionary Technology
bull learner behaviours quantifiable bull interpersonal networks quantifiable
bull discourse quantifiable bull moods and dispositions quantifiable
22
ldquoWe are preparing students for jobs that do not exist yet that will use technologies that have not been invented yet in order to solve problems that are not even problems yetrdquo
ldquoShift Happensrdquo httpshifthappenswikispacescom
23
Learning analytics for this
Learning analytics for this
ldquoWhile employers continue to demand high academic standards they also now want more They want people who can adapt see connections innovate communicate and work with others This is true in many areas of work The new knowledge-based economies in particular will increasingly depend on these abilities Many businesses are paying for courses to promote creative abilities to teach the skills and attitudes that are now essential for economic successhelliprdquo
All our Futures Creativity culture amp education May 1999 24
Learning analytics for this
25
Creativity Culture and Education (2009) Changing Young Lives 2012 Newcastle CCE httpwwwcreativitycultureeducationorgchanging-young-lives-2012
Think about the analytics products and initiatives reviewed above ndash where would you locate them on these dimensions
Expert-led enquiry
Student-led enquiry
Expert-led teaching
Student-led revision
Kno
wle
dge
co
-gen
erat
ion
an
d us
e
Pre-
scrib
ed
Kno
wle
dge
Teacher agency Student agency
Repetition Abstraction Acquisition
Authenticity Agency Identity
Teaching as learning design
The Knowledge-Agency Window
Learning analytics for this
Ruth Deakin Crick Univ Bristol Centre for Systems Learning amp Leadership ldquoPedagogy of Hoperdquo httplearningemergencenet20120921pedagogy-of-hope
analytics grounded in the principles of good
assessment for learning
(not summative assessment for
grading pupils teachers institutions or nations)
27
Assessment for Learning
28
httpassessment-reform-grouporg Few learning analytics are
currently able to take o board the richness of this
original conception of assessment for learning
Assessment for Learning
29
httpassessment-reform-grouporg
Assessment for Learning
30
httpassessment-reform-grouporg
To what extent could automated
feedback be designed and evaluated with
emotional impact in mind
Assessment for Learning
31
httpassessment-reform-grouporg
Can analytics identify proxies
for such advanced qualities
Assessment for Learning
32
httpassessment-reform-grouporg
How do we provide analytics feedback
that does not disempower and de-motivate struggling
learners
analytics for learning conversations
33
Socio-cultural discourse analysis (Mercer et al OU)
bull Disputational talk characterised by disagreement and individualised decision making
bull Cumulative talk in which speakers build positively but uncritically on what the others have said
bull Exploratory talk in which partners engage critically but constructively with each others ideas
34 Mercer N (2004) Sociocultural discourse analysis analysing classroom talk as a social mode of thinking Journal of Applied Linguistics 1(2) 137-168
bull Exploratory talk in which partners engage critically but constructively with each others ideas
bull Statements and suggestions are offered for joint consideration
bull These may be challenged and counter-challenged but challenges are justified and alternative hypotheses are offered
bull Partners all actively participate and opinions are sought and considered before decisions are jointly made
bull Compared with the other two types in Exploratory talk knowledge is made more publicly accountable and reasoning is more visible in the talk
35
Socio-cultural discourse analysis (Mercer et al OU)
Mercer N (2004) Sociocultural discourse analysis analysing classroom talk as a social mode of thinking Journal of Applied Linguistics 1(2) 137-168
Analytics for identifying Exploratory talk
36
Elluminate sessions can be very long ndash lasting for hours or even covering days of a conference
It would be useful if we could identify where quality learning conversations seem to be taking place so we can recommend those sessions and not have to sit through online chat about virtual biscuits
Ferguson R and Buckingham Shum S Learning analytics to identify exploratory dialogue within synchronous text chat 1st International Conference on Learning Analytics amp Knowledge (Banff Canada 27 Mar-1 Apr 2011)
Defining indicators of Exploratory Talk
37
Category Indicator Challenge But if have to respond my view Critique However Irsquom not sure maybe Discussion of resources
Have you read more links
Evaluation Good example good point Explanation Means that our goals Explicit reasoning Next step relates to thatrsquos why Justification I mean we learned we observed Reflections of perspectives of others
Agree here is another makes the point take your point your view
Extract classified as Exploratory Talk
38
Time Contribution 242 PM I hate talking -P My question was whether gadgets were just
basically widgets and we could embed them in various web sites like Netvibes Google Desktop etc
242 PM Thanks thats great I am sure I understood everything but looks inspiring
243 PM Yes why OU tools not generic tools
243 PM Issues of interoperability
243 PM The new SocialLearn site looks a lot like a corkboard where you can add various widgets similar to those existing web start pages
243 PM What if we end up with as many appsgadgets as we have social networks and then we need a recommender for the apps
243 PM My question was on the definition of the crowd in the wisdom of crowds we acsess in the service model
243 PM there are various different flavours of widget eg Google gadgets W3C widgets etc SocialLearn has gone for Google gadgets
-60
-40
-20
0
20
40
60
80
92
8 9
32
93
6
94
0
94
1
94
6
95
0
95
3
95
6
10
00
10
05
10
07
10
07
10
09
10
13
10
17
10
23
10
27
10
31
10
35
10
40
10
45
10
52
10
55
11
04
11
08
11
11
11
17
11
20
11
24
11
26
11
28
11
31
11
32
11
35
11
36
11
38
11
39
11
41
11
44
11
46
11
48
11
52
11
54
12
00
12
03
12
04
12
05
Average Exploratory
Discourse analytics on webinar textchat
Sheffield UK not as sunny as yesterday - still warm Greetings from Hong Kong Morning from Wiltshire sunny here
See you bye for now bye and thank you Bye all for now
Extensions to Ferguson R and Buckingham Shum S (2011) Learning Analytics to Identify Exploratory Dialogue within Synchronous Text Chat Proc 1st Int Conf Learning Analytics amp Knowledge Feb 27-Mar 1 2011 Banff ACM Press Eprint httporoopenacuk28955
Given a 25 hour webinar where in the live textchat were the most effective learning conversations Not at the start and end of a webinar but if we zoom in on a peakhellip
Discourse analytics on webinar textchat
-100
-50
0
50
100
92
8 9
40
9
50
1
000
1
007
1
017
1
031
1
045
1
104
1
117
1
126
1
132
1
138
1
144
1
152
1
203
Averag
Wei amp He extensions to Ferguson R and Buckingham Shum S (2011) Learning Analytics to Identify Exploratory Dialogue within Synchronous Text Chat Proc 1st Int Conf Learning Analytics amp Knowledge Feb 27-Mar 1 2011 Banff ACM Press Eprint httporoopenacuk28955
Classified as ldquoexploratory
talkrdquo
(more substantive for learning)
ldquonon-exploratoryrdquo
Given a 25 hour webinar where in the live textchat were the most effective learning conversations
De Liddo A Buckingham Shum S Quinto I Bachler M and Cannavacciuolo L Discourse-centric learning analytics 1st International Conference on Learning Analytics amp Knowledge (Banff 27 Mar-1 Apr 2011) httporoopenacuk25829
KMirsquos Cohere a web deliberation platform enabling semantic social network and discourse network analytics
Rebecca is playing the role of broker
connecting 2 peersrsquo contributions in meaningful ways
analytics for scholarly writing
42
Discourse analysis (Xerox Incremental Parser)
BACKGROUND KNOWLEDGE
Recent studies indicate hellip
hellip the previously proposed hellip
hellip is universally accepted
NOVELTY
new insights provide direct evidence
we suggest a new approach
results define a novel role
OPEN QUESTION hellip little is known hellip hellip role hellip has been elusive
Current data is insufficient hellip
GENERALIZING emerging as a promising approach Our understanding has grown exponentially growing recognition of the
importance
CONRASTING IDEAS hellip unorthodox view resolves hellip paradoxes hellip
In contrast with previous hypotheses
inconsistent with past findings
SIGNIFICANCE studies have provided important advances
Knowledge is crucial for understanding
valuable information from studies
SURPRISE We have recently observed surprisingly
We have identified unusual The recent discovery suggests intriguing roles
SUMMARIZING The goal of this study Here we show
Altogether our results indicate
Detection of salient sentences in scholarly reports based on the rhetorical signals authors use
Aacutegnes Saacutendor amp OLnet Project httpolnetorgnode512
De Liddo A Saacutendor Aacute and Buckingham Shum S Contested Collective Intelligence Rationale Technologies and a Human-Machine Annotation Study Computer Supported Cooperative Work 21 4-5 (2012) 417-448 httporoopenacuk31052
Human and machine analysis of a text for key contributions
19 sentences annotated 22 sentences annotated 11 sentences same as human annotation
71 sentences annotated 59 sentences annotated 42 sentences same as human annotation
Document 1
Document 2
httptechnologieskmiopenacukcohere20120109cohere-plus-automated-rhetorical-annotation De Liddo A Saacutendor Aacute and Buckingham Shum S Contested Collective Intelligence Rationale Technologies and a Human-Machine Annotation Study Computer Supported Cooperative Work 21 4-5 (2012) 417-448 httporoopenacuk31052
analytics for intepersonal networking
45
Semantic Social Network Analytics
De Liddo A Buckingham Shum S Quinto I Bachler M and Cannavacciuolo L Discourse-centric learning analytics 1st International Conference on Learning Analytics amp Knowledge (Banff 27 Mar-1 Apr 2011) httporoopenacuk25829
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Closing thoughts
54
55
ldquoThe basic question is not what can we measure
The basic question is
what does a good education look likerdquo
(Gardner Campbell)
httpchroniclecomblogstechtherapy20120502episode-95-learning-analytics-could-lead-to-wal-martification-of-college httplak12wikispacescomRecordings
56
Our analytics promote values pedagogy and assessment regimes
Are we clear which master
our analytics serve Are we happy to be judged by them
57
Will learning analytics merely turbocharge the current educational paradigm
mdash which is so often declared
not fit for purposehellip
58
hellipor will learning analytics reflect what we now know about designing authentic
engaged learning developing the new qualities that a
complex society demands
New outcomes we couldnrsquot assess before
Learning Analytics as
Revolutionary Technology
bull learner behaviours quantifiable bull interpersonal networks quantifiable
bull discourse quantifiable bull moods and dispositions quantifiable
22
ldquoWe are preparing students for jobs that do not exist yet that will use technologies that have not been invented yet in order to solve problems that are not even problems yetrdquo
ldquoShift Happensrdquo httpshifthappenswikispacescom
23
Learning analytics for this
Learning analytics for this
ldquoWhile employers continue to demand high academic standards they also now want more They want people who can adapt see connections innovate communicate and work with others This is true in many areas of work The new knowledge-based economies in particular will increasingly depend on these abilities Many businesses are paying for courses to promote creative abilities to teach the skills and attitudes that are now essential for economic successhelliprdquo
All our Futures Creativity culture amp education May 1999 24
Learning analytics for this
25
Creativity Culture and Education (2009) Changing Young Lives 2012 Newcastle CCE httpwwwcreativitycultureeducationorgchanging-young-lives-2012
Think about the analytics products and initiatives reviewed above ndash where would you locate them on these dimensions
Expert-led enquiry
Student-led enquiry
Expert-led teaching
Student-led revision
Kno
wle
dge
co
-gen
erat
ion
an
d us
e
Pre-
scrib
ed
Kno
wle
dge
Teacher agency Student agency
Repetition Abstraction Acquisition
Authenticity Agency Identity
Teaching as learning design
The Knowledge-Agency Window
Learning analytics for this
Ruth Deakin Crick Univ Bristol Centre for Systems Learning amp Leadership ldquoPedagogy of Hoperdquo httplearningemergencenet20120921pedagogy-of-hope
analytics grounded in the principles of good
assessment for learning
(not summative assessment for
grading pupils teachers institutions or nations)
27
Assessment for Learning
28
httpassessment-reform-grouporg Few learning analytics are
currently able to take o board the richness of this
original conception of assessment for learning
Assessment for Learning
29
httpassessment-reform-grouporg
Assessment for Learning
30
httpassessment-reform-grouporg
To what extent could automated
feedback be designed and evaluated with
emotional impact in mind
Assessment for Learning
31
httpassessment-reform-grouporg
Can analytics identify proxies
for such advanced qualities
Assessment for Learning
32
httpassessment-reform-grouporg
How do we provide analytics feedback
that does not disempower and de-motivate struggling
learners
analytics for learning conversations
33
Socio-cultural discourse analysis (Mercer et al OU)
bull Disputational talk characterised by disagreement and individualised decision making
bull Cumulative talk in which speakers build positively but uncritically on what the others have said
bull Exploratory talk in which partners engage critically but constructively with each others ideas
34 Mercer N (2004) Sociocultural discourse analysis analysing classroom talk as a social mode of thinking Journal of Applied Linguistics 1(2) 137-168
bull Exploratory talk in which partners engage critically but constructively with each others ideas
bull Statements and suggestions are offered for joint consideration
bull These may be challenged and counter-challenged but challenges are justified and alternative hypotheses are offered
bull Partners all actively participate and opinions are sought and considered before decisions are jointly made
bull Compared with the other two types in Exploratory talk knowledge is made more publicly accountable and reasoning is more visible in the talk
35
Socio-cultural discourse analysis (Mercer et al OU)
Mercer N (2004) Sociocultural discourse analysis analysing classroom talk as a social mode of thinking Journal of Applied Linguistics 1(2) 137-168
Analytics for identifying Exploratory talk
36
Elluminate sessions can be very long ndash lasting for hours or even covering days of a conference
It would be useful if we could identify where quality learning conversations seem to be taking place so we can recommend those sessions and not have to sit through online chat about virtual biscuits
Ferguson R and Buckingham Shum S Learning analytics to identify exploratory dialogue within synchronous text chat 1st International Conference on Learning Analytics amp Knowledge (Banff Canada 27 Mar-1 Apr 2011)
Defining indicators of Exploratory Talk
37
Category Indicator Challenge But if have to respond my view Critique However Irsquom not sure maybe Discussion of resources
Have you read more links
Evaluation Good example good point Explanation Means that our goals Explicit reasoning Next step relates to thatrsquos why Justification I mean we learned we observed Reflections of perspectives of others
Agree here is another makes the point take your point your view
Extract classified as Exploratory Talk
38
Time Contribution 242 PM I hate talking -P My question was whether gadgets were just
basically widgets and we could embed them in various web sites like Netvibes Google Desktop etc
242 PM Thanks thats great I am sure I understood everything but looks inspiring
243 PM Yes why OU tools not generic tools
243 PM Issues of interoperability
243 PM The new SocialLearn site looks a lot like a corkboard where you can add various widgets similar to those existing web start pages
243 PM What if we end up with as many appsgadgets as we have social networks and then we need a recommender for the apps
243 PM My question was on the definition of the crowd in the wisdom of crowds we acsess in the service model
243 PM there are various different flavours of widget eg Google gadgets W3C widgets etc SocialLearn has gone for Google gadgets
-60
-40
-20
0
20
40
60
80
92
8 9
32
93
6
94
0
94
1
94
6
95
0
95
3
95
6
10
00
10
05
10
07
10
07
10
09
10
13
10
17
10
23
10
27
10
31
10
35
10
40
10
45
10
52
10
55
11
04
11
08
11
11
11
17
11
20
11
24
11
26
11
28
11
31
11
32
11
35
11
36
11
38
11
39
11
41
11
44
11
46
11
48
11
52
11
54
12
00
12
03
12
04
12
05
Average Exploratory
Discourse analytics on webinar textchat
Sheffield UK not as sunny as yesterday - still warm Greetings from Hong Kong Morning from Wiltshire sunny here
See you bye for now bye and thank you Bye all for now
Extensions to Ferguson R and Buckingham Shum S (2011) Learning Analytics to Identify Exploratory Dialogue within Synchronous Text Chat Proc 1st Int Conf Learning Analytics amp Knowledge Feb 27-Mar 1 2011 Banff ACM Press Eprint httporoopenacuk28955
Given a 25 hour webinar where in the live textchat were the most effective learning conversations Not at the start and end of a webinar but if we zoom in on a peakhellip
Discourse analytics on webinar textchat
-100
-50
0
50
100
92
8 9
40
9
50
1
000
1
007
1
017
1
031
1
045
1
104
1
117
1
126
1
132
1
138
1
144
1
152
1
203
Averag
Wei amp He extensions to Ferguson R and Buckingham Shum S (2011) Learning Analytics to Identify Exploratory Dialogue within Synchronous Text Chat Proc 1st Int Conf Learning Analytics amp Knowledge Feb 27-Mar 1 2011 Banff ACM Press Eprint httporoopenacuk28955
Classified as ldquoexploratory
talkrdquo
(more substantive for learning)
ldquonon-exploratoryrdquo
Given a 25 hour webinar where in the live textchat were the most effective learning conversations
De Liddo A Buckingham Shum S Quinto I Bachler M and Cannavacciuolo L Discourse-centric learning analytics 1st International Conference on Learning Analytics amp Knowledge (Banff 27 Mar-1 Apr 2011) httporoopenacuk25829
KMirsquos Cohere a web deliberation platform enabling semantic social network and discourse network analytics
Rebecca is playing the role of broker
connecting 2 peersrsquo contributions in meaningful ways
analytics for scholarly writing
42
Discourse analysis (Xerox Incremental Parser)
BACKGROUND KNOWLEDGE
Recent studies indicate hellip
hellip the previously proposed hellip
hellip is universally accepted
NOVELTY
new insights provide direct evidence
we suggest a new approach
results define a novel role
OPEN QUESTION hellip little is known hellip hellip role hellip has been elusive
Current data is insufficient hellip
GENERALIZING emerging as a promising approach Our understanding has grown exponentially growing recognition of the
importance
CONRASTING IDEAS hellip unorthodox view resolves hellip paradoxes hellip
In contrast with previous hypotheses
inconsistent with past findings
SIGNIFICANCE studies have provided important advances
Knowledge is crucial for understanding
valuable information from studies
SURPRISE We have recently observed surprisingly
We have identified unusual The recent discovery suggests intriguing roles
SUMMARIZING The goal of this study Here we show
Altogether our results indicate
Detection of salient sentences in scholarly reports based on the rhetorical signals authors use
Aacutegnes Saacutendor amp OLnet Project httpolnetorgnode512
De Liddo A Saacutendor Aacute and Buckingham Shum S Contested Collective Intelligence Rationale Technologies and a Human-Machine Annotation Study Computer Supported Cooperative Work 21 4-5 (2012) 417-448 httporoopenacuk31052
Human and machine analysis of a text for key contributions
19 sentences annotated 22 sentences annotated 11 sentences same as human annotation
71 sentences annotated 59 sentences annotated 42 sentences same as human annotation
Document 1
Document 2
httptechnologieskmiopenacukcohere20120109cohere-plus-automated-rhetorical-annotation De Liddo A Saacutendor Aacute and Buckingham Shum S Contested Collective Intelligence Rationale Technologies and a Human-Machine Annotation Study Computer Supported Cooperative Work 21 4-5 (2012) 417-448 httporoopenacuk31052
analytics for intepersonal networking
45
Semantic Social Network Analytics
De Liddo A Buckingham Shum S Quinto I Bachler M and Cannavacciuolo L Discourse-centric learning analytics 1st International Conference on Learning Analytics amp Knowledge (Banff 27 Mar-1 Apr 2011) httporoopenacuk25829
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Closing thoughts
54
55
ldquoThe basic question is not what can we measure
The basic question is
what does a good education look likerdquo
(Gardner Campbell)
httpchroniclecomblogstechtherapy20120502episode-95-learning-analytics-could-lead-to-wal-martification-of-college httplak12wikispacescomRecordings
56
Our analytics promote values pedagogy and assessment regimes
Are we clear which master
our analytics serve Are we happy to be judged by them
57
Will learning analytics merely turbocharge the current educational paradigm
mdash which is so often declared
not fit for purposehellip
58
hellipor will learning analytics reflect what we now know about designing authentic
engaged learning developing the new qualities that a
complex society demands
ldquoWe are preparing students for jobs that do not exist yet that will use technologies that have not been invented yet in order to solve problems that are not even problems yetrdquo
ldquoShift Happensrdquo httpshifthappenswikispacescom
23
Learning analytics for this
Learning analytics for this
ldquoWhile employers continue to demand high academic standards they also now want more They want people who can adapt see connections innovate communicate and work with others This is true in many areas of work The new knowledge-based economies in particular will increasingly depend on these abilities Many businesses are paying for courses to promote creative abilities to teach the skills and attitudes that are now essential for economic successhelliprdquo
All our Futures Creativity culture amp education May 1999 24
Learning analytics for this
25
Creativity Culture and Education (2009) Changing Young Lives 2012 Newcastle CCE httpwwwcreativitycultureeducationorgchanging-young-lives-2012
Think about the analytics products and initiatives reviewed above ndash where would you locate them on these dimensions
Expert-led enquiry
Student-led enquiry
Expert-led teaching
Student-led revision
Kno
wle
dge
co
-gen
erat
ion
an
d us
e
Pre-
scrib
ed
Kno
wle
dge
Teacher agency Student agency
Repetition Abstraction Acquisition
Authenticity Agency Identity
Teaching as learning design
The Knowledge-Agency Window
Learning analytics for this
Ruth Deakin Crick Univ Bristol Centre for Systems Learning amp Leadership ldquoPedagogy of Hoperdquo httplearningemergencenet20120921pedagogy-of-hope
analytics grounded in the principles of good
assessment for learning
(not summative assessment for
grading pupils teachers institutions or nations)
27
Assessment for Learning
28
httpassessment-reform-grouporg Few learning analytics are
currently able to take o board the richness of this
original conception of assessment for learning
Assessment for Learning
29
httpassessment-reform-grouporg
Assessment for Learning
30
httpassessment-reform-grouporg
To what extent could automated
feedback be designed and evaluated with
emotional impact in mind
Assessment for Learning
31
httpassessment-reform-grouporg
Can analytics identify proxies
for such advanced qualities
Assessment for Learning
32
httpassessment-reform-grouporg
How do we provide analytics feedback
that does not disempower and de-motivate struggling
learners
analytics for learning conversations
33
Socio-cultural discourse analysis (Mercer et al OU)
bull Disputational talk characterised by disagreement and individualised decision making
bull Cumulative talk in which speakers build positively but uncritically on what the others have said
bull Exploratory talk in which partners engage critically but constructively with each others ideas
34 Mercer N (2004) Sociocultural discourse analysis analysing classroom talk as a social mode of thinking Journal of Applied Linguistics 1(2) 137-168
bull Exploratory talk in which partners engage critically but constructively with each others ideas
bull Statements and suggestions are offered for joint consideration
bull These may be challenged and counter-challenged but challenges are justified and alternative hypotheses are offered
bull Partners all actively participate and opinions are sought and considered before decisions are jointly made
bull Compared with the other two types in Exploratory talk knowledge is made more publicly accountable and reasoning is more visible in the talk
35
Socio-cultural discourse analysis (Mercer et al OU)
Mercer N (2004) Sociocultural discourse analysis analysing classroom talk as a social mode of thinking Journal of Applied Linguistics 1(2) 137-168
Analytics for identifying Exploratory talk
36
Elluminate sessions can be very long ndash lasting for hours or even covering days of a conference
It would be useful if we could identify where quality learning conversations seem to be taking place so we can recommend those sessions and not have to sit through online chat about virtual biscuits
Ferguson R and Buckingham Shum S Learning analytics to identify exploratory dialogue within synchronous text chat 1st International Conference on Learning Analytics amp Knowledge (Banff Canada 27 Mar-1 Apr 2011)
Defining indicators of Exploratory Talk
37
Category Indicator Challenge But if have to respond my view Critique However Irsquom not sure maybe Discussion of resources
Have you read more links
Evaluation Good example good point Explanation Means that our goals Explicit reasoning Next step relates to thatrsquos why Justification I mean we learned we observed Reflections of perspectives of others
Agree here is another makes the point take your point your view
Extract classified as Exploratory Talk
38
Time Contribution 242 PM I hate talking -P My question was whether gadgets were just
basically widgets and we could embed them in various web sites like Netvibes Google Desktop etc
242 PM Thanks thats great I am sure I understood everything but looks inspiring
243 PM Yes why OU tools not generic tools
243 PM Issues of interoperability
243 PM The new SocialLearn site looks a lot like a corkboard where you can add various widgets similar to those existing web start pages
243 PM What if we end up with as many appsgadgets as we have social networks and then we need a recommender for the apps
243 PM My question was on the definition of the crowd in the wisdom of crowds we acsess in the service model
243 PM there are various different flavours of widget eg Google gadgets W3C widgets etc SocialLearn has gone for Google gadgets
-60
-40
-20
0
20
40
60
80
92
8 9
32
93
6
94
0
94
1
94
6
95
0
95
3
95
6
10
00
10
05
10
07
10
07
10
09
10
13
10
17
10
23
10
27
10
31
10
35
10
40
10
45
10
52
10
55
11
04
11
08
11
11
11
17
11
20
11
24
11
26
11
28
11
31
11
32
11
35
11
36
11
38
11
39
11
41
11
44
11
46
11
48
11
52
11
54
12
00
12
03
12
04
12
05
Average Exploratory
Discourse analytics on webinar textchat
Sheffield UK not as sunny as yesterday - still warm Greetings from Hong Kong Morning from Wiltshire sunny here
See you bye for now bye and thank you Bye all for now
Extensions to Ferguson R and Buckingham Shum S (2011) Learning Analytics to Identify Exploratory Dialogue within Synchronous Text Chat Proc 1st Int Conf Learning Analytics amp Knowledge Feb 27-Mar 1 2011 Banff ACM Press Eprint httporoopenacuk28955
Given a 25 hour webinar where in the live textchat were the most effective learning conversations Not at the start and end of a webinar but if we zoom in on a peakhellip
Discourse analytics on webinar textchat
-100
-50
0
50
100
92
8 9
40
9
50
1
000
1
007
1
017
1
031
1
045
1
104
1
117
1
126
1
132
1
138
1
144
1
152
1
203
Averag
Wei amp He extensions to Ferguson R and Buckingham Shum S (2011) Learning Analytics to Identify Exploratory Dialogue within Synchronous Text Chat Proc 1st Int Conf Learning Analytics amp Knowledge Feb 27-Mar 1 2011 Banff ACM Press Eprint httporoopenacuk28955
Classified as ldquoexploratory
talkrdquo
(more substantive for learning)
ldquonon-exploratoryrdquo
Given a 25 hour webinar where in the live textchat were the most effective learning conversations
De Liddo A Buckingham Shum S Quinto I Bachler M and Cannavacciuolo L Discourse-centric learning analytics 1st International Conference on Learning Analytics amp Knowledge (Banff 27 Mar-1 Apr 2011) httporoopenacuk25829
KMirsquos Cohere a web deliberation platform enabling semantic social network and discourse network analytics
Rebecca is playing the role of broker
connecting 2 peersrsquo contributions in meaningful ways
analytics for scholarly writing
42
Discourse analysis (Xerox Incremental Parser)
BACKGROUND KNOWLEDGE
Recent studies indicate hellip
hellip the previously proposed hellip
hellip is universally accepted
NOVELTY
new insights provide direct evidence
we suggest a new approach
results define a novel role
OPEN QUESTION hellip little is known hellip hellip role hellip has been elusive
Current data is insufficient hellip
GENERALIZING emerging as a promising approach Our understanding has grown exponentially growing recognition of the
importance
CONRASTING IDEAS hellip unorthodox view resolves hellip paradoxes hellip
In contrast with previous hypotheses
inconsistent with past findings
SIGNIFICANCE studies have provided important advances
Knowledge is crucial for understanding
valuable information from studies
SURPRISE We have recently observed surprisingly
We have identified unusual The recent discovery suggests intriguing roles
SUMMARIZING The goal of this study Here we show
Altogether our results indicate
Detection of salient sentences in scholarly reports based on the rhetorical signals authors use
Aacutegnes Saacutendor amp OLnet Project httpolnetorgnode512
De Liddo A Saacutendor Aacute and Buckingham Shum S Contested Collective Intelligence Rationale Technologies and a Human-Machine Annotation Study Computer Supported Cooperative Work 21 4-5 (2012) 417-448 httporoopenacuk31052
Human and machine analysis of a text for key contributions
19 sentences annotated 22 sentences annotated 11 sentences same as human annotation
71 sentences annotated 59 sentences annotated 42 sentences same as human annotation
Document 1
Document 2
httptechnologieskmiopenacukcohere20120109cohere-plus-automated-rhetorical-annotation De Liddo A Saacutendor Aacute and Buckingham Shum S Contested Collective Intelligence Rationale Technologies and a Human-Machine Annotation Study Computer Supported Cooperative Work 21 4-5 (2012) 417-448 httporoopenacuk31052
analytics for intepersonal networking
45
Semantic Social Network Analytics
De Liddo A Buckingham Shum S Quinto I Bachler M and Cannavacciuolo L Discourse-centric learning analytics 1st International Conference on Learning Analytics amp Knowledge (Banff 27 Mar-1 Apr 2011) httporoopenacuk25829
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Closing thoughts
54
55
ldquoThe basic question is not what can we measure
The basic question is
what does a good education look likerdquo
(Gardner Campbell)
httpchroniclecomblogstechtherapy20120502episode-95-learning-analytics-could-lead-to-wal-martification-of-college httplak12wikispacescomRecordings
56
Our analytics promote values pedagogy and assessment regimes
Are we clear which master
our analytics serve Are we happy to be judged by them
57
Will learning analytics merely turbocharge the current educational paradigm
mdash which is so often declared
not fit for purposehellip
58
hellipor will learning analytics reflect what we now know about designing authentic
engaged learning developing the new qualities that a
complex society demands
Learning analytics for this
ldquoWhile employers continue to demand high academic standards they also now want more They want people who can adapt see connections innovate communicate and work with others This is true in many areas of work The new knowledge-based economies in particular will increasingly depend on these abilities Many businesses are paying for courses to promote creative abilities to teach the skills and attitudes that are now essential for economic successhelliprdquo
All our Futures Creativity culture amp education May 1999 24
Learning analytics for this
25
Creativity Culture and Education (2009) Changing Young Lives 2012 Newcastle CCE httpwwwcreativitycultureeducationorgchanging-young-lives-2012
Think about the analytics products and initiatives reviewed above ndash where would you locate them on these dimensions
Expert-led enquiry
Student-led enquiry
Expert-led teaching
Student-led revision
Kno
wle
dge
co
-gen
erat
ion
an
d us
e
Pre-
scrib
ed
Kno
wle
dge
Teacher agency Student agency
Repetition Abstraction Acquisition
Authenticity Agency Identity
Teaching as learning design
The Knowledge-Agency Window
Learning analytics for this
Ruth Deakin Crick Univ Bristol Centre for Systems Learning amp Leadership ldquoPedagogy of Hoperdquo httplearningemergencenet20120921pedagogy-of-hope
analytics grounded in the principles of good
assessment for learning
(not summative assessment for
grading pupils teachers institutions or nations)
27
Assessment for Learning
28
httpassessment-reform-grouporg Few learning analytics are
currently able to take o board the richness of this
original conception of assessment for learning
Assessment for Learning
29
httpassessment-reform-grouporg
Assessment for Learning
30
httpassessment-reform-grouporg
To what extent could automated
feedback be designed and evaluated with
emotional impact in mind
Assessment for Learning
31
httpassessment-reform-grouporg
Can analytics identify proxies
for such advanced qualities
Assessment for Learning
32
httpassessment-reform-grouporg
How do we provide analytics feedback
that does not disempower and de-motivate struggling
learners
analytics for learning conversations
33
Socio-cultural discourse analysis (Mercer et al OU)
bull Disputational talk characterised by disagreement and individualised decision making
bull Cumulative talk in which speakers build positively but uncritically on what the others have said
bull Exploratory talk in which partners engage critically but constructively with each others ideas
34 Mercer N (2004) Sociocultural discourse analysis analysing classroom talk as a social mode of thinking Journal of Applied Linguistics 1(2) 137-168
bull Exploratory talk in which partners engage critically but constructively with each others ideas
bull Statements and suggestions are offered for joint consideration
bull These may be challenged and counter-challenged but challenges are justified and alternative hypotheses are offered
bull Partners all actively participate and opinions are sought and considered before decisions are jointly made
bull Compared with the other two types in Exploratory talk knowledge is made more publicly accountable and reasoning is more visible in the talk
35
Socio-cultural discourse analysis (Mercer et al OU)
Mercer N (2004) Sociocultural discourse analysis analysing classroom talk as a social mode of thinking Journal of Applied Linguistics 1(2) 137-168
Analytics for identifying Exploratory talk
36
Elluminate sessions can be very long ndash lasting for hours or even covering days of a conference
It would be useful if we could identify where quality learning conversations seem to be taking place so we can recommend those sessions and not have to sit through online chat about virtual biscuits
Ferguson R and Buckingham Shum S Learning analytics to identify exploratory dialogue within synchronous text chat 1st International Conference on Learning Analytics amp Knowledge (Banff Canada 27 Mar-1 Apr 2011)
Defining indicators of Exploratory Talk
37
Category Indicator Challenge But if have to respond my view Critique However Irsquom not sure maybe Discussion of resources
Have you read more links
Evaluation Good example good point Explanation Means that our goals Explicit reasoning Next step relates to thatrsquos why Justification I mean we learned we observed Reflections of perspectives of others
Agree here is another makes the point take your point your view
Extract classified as Exploratory Talk
38
Time Contribution 242 PM I hate talking -P My question was whether gadgets were just
basically widgets and we could embed them in various web sites like Netvibes Google Desktop etc
242 PM Thanks thats great I am sure I understood everything but looks inspiring
243 PM Yes why OU tools not generic tools
243 PM Issues of interoperability
243 PM The new SocialLearn site looks a lot like a corkboard where you can add various widgets similar to those existing web start pages
243 PM What if we end up with as many appsgadgets as we have social networks and then we need a recommender for the apps
243 PM My question was on the definition of the crowd in the wisdom of crowds we acsess in the service model
243 PM there are various different flavours of widget eg Google gadgets W3C widgets etc SocialLearn has gone for Google gadgets
-60
-40
-20
0
20
40
60
80
92
8 9
32
93
6
94
0
94
1
94
6
95
0
95
3
95
6
10
00
10
05
10
07
10
07
10
09
10
13
10
17
10
23
10
27
10
31
10
35
10
40
10
45
10
52
10
55
11
04
11
08
11
11
11
17
11
20
11
24
11
26
11
28
11
31
11
32
11
35
11
36
11
38
11
39
11
41
11
44
11
46
11
48
11
52
11
54
12
00
12
03
12
04
12
05
Average Exploratory
Discourse analytics on webinar textchat
Sheffield UK not as sunny as yesterday - still warm Greetings from Hong Kong Morning from Wiltshire sunny here
See you bye for now bye and thank you Bye all for now
Extensions to Ferguson R and Buckingham Shum S (2011) Learning Analytics to Identify Exploratory Dialogue within Synchronous Text Chat Proc 1st Int Conf Learning Analytics amp Knowledge Feb 27-Mar 1 2011 Banff ACM Press Eprint httporoopenacuk28955
Given a 25 hour webinar where in the live textchat were the most effective learning conversations Not at the start and end of a webinar but if we zoom in on a peakhellip
Discourse analytics on webinar textchat
-100
-50
0
50
100
92
8 9
40
9
50
1
000
1
007
1
017
1
031
1
045
1
104
1
117
1
126
1
132
1
138
1
144
1
152
1
203
Averag
Wei amp He extensions to Ferguson R and Buckingham Shum S (2011) Learning Analytics to Identify Exploratory Dialogue within Synchronous Text Chat Proc 1st Int Conf Learning Analytics amp Knowledge Feb 27-Mar 1 2011 Banff ACM Press Eprint httporoopenacuk28955
Classified as ldquoexploratory
talkrdquo
(more substantive for learning)
ldquonon-exploratoryrdquo
Given a 25 hour webinar where in the live textchat were the most effective learning conversations
De Liddo A Buckingham Shum S Quinto I Bachler M and Cannavacciuolo L Discourse-centric learning analytics 1st International Conference on Learning Analytics amp Knowledge (Banff 27 Mar-1 Apr 2011) httporoopenacuk25829
KMirsquos Cohere a web deliberation platform enabling semantic social network and discourse network analytics
Rebecca is playing the role of broker
connecting 2 peersrsquo contributions in meaningful ways
analytics for scholarly writing
42
Discourse analysis (Xerox Incremental Parser)
BACKGROUND KNOWLEDGE
Recent studies indicate hellip
hellip the previously proposed hellip
hellip is universally accepted
NOVELTY
new insights provide direct evidence
we suggest a new approach
results define a novel role
OPEN QUESTION hellip little is known hellip hellip role hellip has been elusive
Current data is insufficient hellip
GENERALIZING emerging as a promising approach Our understanding has grown exponentially growing recognition of the
importance
CONRASTING IDEAS hellip unorthodox view resolves hellip paradoxes hellip
In contrast with previous hypotheses
inconsistent with past findings
SIGNIFICANCE studies have provided important advances
Knowledge is crucial for understanding
valuable information from studies
SURPRISE We have recently observed surprisingly
We have identified unusual The recent discovery suggests intriguing roles
SUMMARIZING The goal of this study Here we show
Altogether our results indicate
Detection of salient sentences in scholarly reports based on the rhetorical signals authors use
Aacutegnes Saacutendor amp OLnet Project httpolnetorgnode512
De Liddo A Saacutendor Aacute and Buckingham Shum S Contested Collective Intelligence Rationale Technologies and a Human-Machine Annotation Study Computer Supported Cooperative Work 21 4-5 (2012) 417-448 httporoopenacuk31052
Human and machine analysis of a text for key contributions
19 sentences annotated 22 sentences annotated 11 sentences same as human annotation
71 sentences annotated 59 sentences annotated 42 sentences same as human annotation
Document 1
Document 2
httptechnologieskmiopenacukcohere20120109cohere-plus-automated-rhetorical-annotation De Liddo A Saacutendor Aacute and Buckingham Shum S Contested Collective Intelligence Rationale Technologies and a Human-Machine Annotation Study Computer Supported Cooperative Work 21 4-5 (2012) 417-448 httporoopenacuk31052
analytics for intepersonal networking
45
Semantic Social Network Analytics
De Liddo A Buckingham Shum S Quinto I Bachler M and Cannavacciuolo L Discourse-centric learning analytics 1st International Conference on Learning Analytics amp Knowledge (Banff 27 Mar-1 Apr 2011) httporoopenacuk25829
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Closing thoughts
54
55
ldquoThe basic question is not what can we measure
The basic question is
what does a good education look likerdquo
(Gardner Campbell)
httpchroniclecomblogstechtherapy20120502episode-95-learning-analytics-could-lead-to-wal-martification-of-college httplak12wikispacescomRecordings
56
Our analytics promote values pedagogy and assessment regimes
Are we clear which master
our analytics serve Are we happy to be judged by them
57
Will learning analytics merely turbocharge the current educational paradigm
mdash which is so often declared
not fit for purposehellip
58
hellipor will learning analytics reflect what we now know about designing authentic
engaged learning developing the new qualities that a
complex society demands
Learning analytics for this
25
Creativity Culture and Education (2009) Changing Young Lives 2012 Newcastle CCE httpwwwcreativitycultureeducationorgchanging-young-lives-2012
Think about the analytics products and initiatives reviewed above ndash where would you locate them on these dimensions
Expert-led enquiry
Student-led enquiry
Expert-led teaching
Student-led revision
Kno
wle
dge
co
-gen
erat
ion
an
d us
e
Pre-
scrib
ed
Kno
wle
dge
Teacher agency Student agency
Repetition Abstraction Acquisition
Authenticity Agency Identity
Teaching as learning design
The Knowledge-Agency Window
Learning analytics for this
Ruth Deakin Crick Univ Bristol Centre for Systems Learning amp Leadership ldquoPedagogy of Hoperdquo httplearningemergencenet20120921pedagogy-of-hope
analytics grounded in the principles of good
assessment for learning
(not summative assessment for
grading pupils teachers institutions or nations)
27
Assessment for Learning
28
httpassessment-reform-grouporg Few learning analytics are
currently able to take o board the richness of this
original conception of assessment for learning
Assessment for Learning
29
httpassessment-reform-grouporg
Assessment for Learning
30
httpassessment-reform-grouporg
To what extent could automated
feedback be designed and evaluated with
emotional impact in mind
Assessment for Learning
31
httpassessment-reform-grouporg
Can analytics identify proxies
for such advanced qualities
Assessment for Learning
32
httpassessment-reform-grouporg
How do we provide analytics feedback
that does not disempower and de-motivate struggling
learners
analytics for learning conversations
33
Socio-cultural discourse analysis (Mercer et al OU)
bull Disputational talk characterised by disagreement and individualised decision making
bull Cumulative talk in which speakers build positively but uncritically on what the others have said
bull Exploratory talk in which partners engage critically but constructively with each others ideas
34 Mercer N (2004) Sociocultural discourse analysis analysing classroom talk as a social mode of thinking Journal of Applied Linguistics 1(2) 137-168
bull Exploratory talk in which partners engage critically but constructively with each others ideas
bull Statements and suggestions are offered for joint consideration
bull These may be challenged and counter-challenged but challenges are justified and alternative hypotheses are offered
bull Partners all actively participate and opinions are sought and considered before decisions are jointly made
bull Compared with the other two types in Exploratory talk knowledge is made more publicly accountable and reasoning is more visible in the talk
35
Socio-cultural discourse analysis (Mercer et al OU)
Mercer N (2004) Sociocultural discourse analysis analysing classroom talk as a social mode of thinking Journal of Applied Linguistics 1(2) 137-168
Analytics for identifying Exploratory talk
36
Elluminate sessions can be very long ndash lasting for hours or even covering days of a conference
It would be useful if we could identify where quality learning conversations seem to be taking place so we can recommend those sessions and not have to sit through online chat about virtual biscuits
Ferguson R and Buckingham Shum S Learning analytics to identify exploratory dialogue within synchronous text chat 1st International Conference on Learning Analytics amp Knowledge (Banff Canada 27 Mar-1 Apr 2011)
Defining indicators of Exploratory Talk
37
Category Indicator Challenge But if have to respond my view Critique However Irsquom not sure maybe Discussion of resources
Have you read more links
Evaluation Good example good point Explanation Means that our goals Explicit reasoning Next step relates to thatrsquos why Justification I mean we learned we observed Reflections of perspectives of others
Agree here is another makes the point take your point your view
Extract classified as Exploratory Talk
38
Time Contribution 242 PM I hate talking -P My question was whether gadgets were just
basically widgets and we could embed them in various web sites like Netvibes Google Desktop etc
242 PM Thanks thats great I am sure I understood everything but looks inspiring
243 PM Yes why OU tools not generic tools
243 PM Issues of interoperability
243 PM The new SocialLearn site looks a lot like a corkboard where you can add various widgets similar to those existing web start pages
243 PM What if we end up with as many appsgadgets as we have social networks and then we need a recommender for the apps
243 PM My question was on the definition of the crowd in the wisdom of crowds we acsess in the service model
243 PM there are various different flavours of widget eg Google gadgets W3C widgets etc SocialLearn has gone for Google gadgets
-60
-40
-20
0
20
40
60
80
92
8 9
32
93
6
94
0
94
1
94
6
95
0
95
3
95
6
10
00
10
05
10
07
10
07
10
09
10
13
10
17
10
23
10
27
10
31
10
35
10
40
10
45
10
52
10
55
11
04
11
08
11
11
11
17
11
20
11
24
11
26
11
28
11
31
11
32
11
35
11
36
11
38
11
39
11
41
11
44
11
46
11
48
11
52
11
54
12
00
12
03
12
04
12
05
Average Exploratory
Discourse analytics on webinar textchat
Sheffield UK not as sunny as yesterday - still warm Greetings from Hong Kong Morning from Wiltshire sunny here
See you bye for now bye and thank you Bye all for now
Extensions to Ferguson R and Buckingham Shum S (2011) Learning Analytics to Identify Exploratory Dialogue within Synchronous Text Chat Proc 1st Int Conf Learning Analytics amp Knowledge Feb 27-Mar 1 2011 Banff ACM Press Eprint httporoopenacuk28955
Given a 25 hour webinar where in the live textchat were the most effective learning conversations Not at the start and end of a webinar but if we zoom in on a peakhellip
Discourse analytics on webinar textchat
-100
-50
0
50
100
92
8 9
40
9
50
1
000
1
007
1
017
1
031
1
045
1
104
1
117
1
126
1
132
1
138
1
144
1
152
1
203
Averag
Wei amp He extensions to Ferguson R and Buckingham Shum S (2011) Learning Analytics to Identify Exploratory Dialogue within Synchronous Text Chat Proc 1st Int Conf Learning Analytics amp Knowledge Feb 27-Mar 1 2011 Banff ACM Press Eprint httporoopenacuk28955
Classified as ldquoexploratory
talkrdquo
(more substantive for learning)
ldquonon-exploratoryrdquo
Given a 25 hour webinar where in the live textchat were the most effective learning conversations
De Liddo A Buckingham Shum S Quinto I Bachler M and Cannavacciuolo L Discourse-centric learning analytics 1st International Conference on Learning Analytics amp Knowledge (Banff 27 Mar-1 Apr 2011) httporoopenacuk25829
KMirsquos Cohere a web deliberation platform enabling semantic social network and discourse network analytics
Rebecca is playing the role of broker
connecting 2 peersrsquo contributions in meaningful ways
analytics for scholarly writing
42
Discourse analysis (Xerox Incremental Parser)
BACKGROUND KNOWLEDGE
Recent studies indicate hellip
hellip the previously proposed hellip
hellip is universally accepted
NOVELTY
new insights provide direct evidence
we suggest a new approach
results define a novel role
OPEN QUESTION hellip little is known hellip hellip role hellip has been elusive
Current data is insufficient hellip
GENERALIZING emerging as a promising approach Our understanding has grown exponentially growing recognition of the
importance
CONRASTING IDEAS hellip unorthodox view resolves hellip paradoxes hellip
In contrast with previous hypotheses
inconsistent with past findings
SIGNIFICANCE studies have provided important advances
Knowledge is crucial for understanding
valuable information from studies
SURPRISE We have recently observed surprisingly
We have identified unusual The recent discovery suggests intriguing roles
SUMMARIZING The goal of this study Here we show
Altogether our results indicate
Detection of salient sentences in scholarly reports based on the rhetorical signals authors use
Aacutegnes Saacutendor amp OLnet Project httpolnetorgnode512
De Liddo A Saacutendor Aacute and Buckingham Shum S Contested Collective Intelligence Rationale Technologies and a Human-Machine Annotation Study Computer Supported Cooperative Work 21 4-5 (2012) 417-448 httporoopenacuk31052
Human and machine analysis of a text for key contributions
19 sentences annotated 22 sentences annotated 11 sentences same as human annotation
71 sentences annotated 59 sentences annotated 42 sentences same as human annotation
Document 1
Document 2
httptechnologieskmiopenacukcohere20120109cohere-plus-automated-rhetorical-annotation De Liddo A Saacutendor Aacute and Buckingham Shum S Contested Collective Intelligence Rationale Technologies and a Human-Machine Annotation Study Computer Supported Cooperative Work 21 4-5 (2012) 417-448 httporoopenacuk31052
analytics for intepersonal networking
45
Semantic Social Network Analytics
De Liddo A Buckingham Shum S Quinto I Bachler M and Cannavacciuolo L Discourse-centric learning analytics 1st International Conference on Learning Analytics amp Knowledge (Banff 27 Mar-1 Apr 2011) httporoopenacuk25829
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Closing thoughts
54
55
ldquoThe basic question is not what can we measure
The basic question is
what does a good education look likerdquo
(Gardner Campbell)
httpchroniclecomblogstechtherapy20120502episode-95-learning-analytics-could-lead-to-wal-martification-of-college httplak12wikispacescomRecordings
56
Our analytics promote values pedagogy and assessment regimes
Are we clear which master
our analytics serve Are we happy to be judged by them
57
Will learning analytics merely turbocharge the current educational paradigm
mdash which is so often declared
not fit for purposehellip
58
hellipor will learning analytics reflect what we now know about designing authentic
engaged learning developing the new qualities that a
complex society demands
Expert-led enquiry
Student-led enquiry
Expert-led teaching
Student-led revision
Kno
wle
dge
co
-gen
erat
ion
an
d us
e
Pre-
scrib
ed
Kno
wle
dge
Teacher agency Student agency
Repetition Abstraction Acquisition
Authenticity Agency Identity
Teaching as learning design
The Knowledge-Agency Window
Learning analytics for this
Ruth Deakin Crick Univ Bristol Centre for Systems Learning amp Leadership ldquoPedagogy of Hoperdquo httplearningemergencenet20120921pedagogy-of-hope
analytics grounded in the principles of good
assessment for learning
(not summative assessment for
grading pupils teachers institutions or nations)
27
Assessment for Learning
28
httpassessment-reform-grouporg Few learning analytics are
currently able to take o board the richness of this
original conception of assessment for learning
Assessment for Learning
29
httpassessment-reform-grouporg
Assessment for Learning
30
httpassessment-reform-grouporg
To what extent could automated
feedback be designed and evaluated with
emotional impact in mind
Assessment for Learning
31
httpassessment-reform-grouporg
Can analytics identify proxies
for such advanced qualities
Assessment for Learning
32
httpassessment-reform-grouporg
How do we provide analytics feedback
that does not disempower and de-motivate struggling
learners
analytics for learning conversations
33
Socio-cultural discourse analysis (Mercer et al OU)
bull Disputational talk characterised by disagreement and individualised decision making
bull Cumulative talk in which speakers build positively but uncritically on what the others have said
bull Exploratory talk in which partners engage critically but constructively with each others ideas
34 Mercer N (2004) Sociocultural discourse analysis analysing classroom talk as a social mode of thinking Journal of Applied Linguistics 1(2) 137-168
bull Exploratory talk in which partners engage critically but constructively with each others ideas
bull Statements and suggestions are offered for joint consideration
bull These may be challenged and counter-challenged but challenges are justified and alternative hypotheses are offered
bull Partners all actively participate and opinions are sought and considered before decisions are jointly made
bull Compared with the other two types in Exploratory talk knowledge is made more publicly accountable and reasoning is more visible in the talk
35
Socio-cultural discourse analysis (Mercer et al OU)
Mercer N (2004) Sociocultural discourse analysis analysing classroom talk as a social mode of thinking Journal of Applied Linguistics 1(2) 137-168
Analytics for identifying Exploratory talk
36
Elluminate sessions can be very long ndash lasting for hours or even covering days of a conference
It would be useful if we could identify where quality learning conversations seem to be taking place so we can recommend those sessions and not have to sit through online chat about virtual biscuits
Ferguson R and Buckingham Shum S Learning analytics to identify exploratory dialogue within synchronous text chat 1st International Conference on Learning Analytics amp Knowledge (Banff Canada 27 Mar-1 Apr 2011)
Defining indicators of Exploratory Talk
37
Category Indicator Challenge But if have to respond my view Critique However Irsquom not sure maybe Discussion of resources
Have you read more links
Evaluation Good example good point Explanation Means that our goals Explicit reasoning Next step relates to thatrsquos why Justification I mean we learned we observed Reflections of perspectives of others
Agree here is another makes the point take your point your view
Extract classified as Exploratory Talk
38
Time Contribution 242 PM I hate talking -P My question was whether gadgets were just
basically widgets and we could embed them in various web sites like Netvibes Google Desktop etc
242 PM Thanks thats great I am sure I understood everything but looks inspiring
243 PM Yes why OU tools not generic tools
243 PM Issues of interoperability
243 PM The new SocialLearn site looks a lot like a corkboard where you can add various widgets similar to those existing web start pages
243 PM What if we end up with as many appsgadgets as we have social networks and then we need a recommender for the apps
243 PM My question was on the definition of the crowd in the wisdom of crowds we acsess in the service model
243 PM there are various different flavours of widget eg Google gadgets W3C widgets etc SocialLearn has gone for Google gadgets
-60
-40
-20
0
20
40
60
80
92
8 9
32
93
6
94
0
94
1
94
6
95
0
95
3
95
6
10
00
10
05
10
07
10
07
10
09
10
13
10
17
10
23
10
27
10
31
10
35
10
40
10
45
10
52
10
55
11
04
11
08
11
11
11
17
11
20
11
24
11
26
11
28
11
31
11
32
11
35
11
36
11
38
11
39
11
41
11
44
11
46
11
48
11
52
11
54
12
00
12
03
12
04
12
05
Average Exploratory
Discourse analytics on webinar textchat
Sheffield UK not as sunny as yesterday - still warm Greetings from Hong Kong Morning from Wiltshire sunny here
See you bye for now bye and thank you Bye all for now
Extensions to Ferguson R and Buckingham Shum S (2011) Learning Analytics to Identify Exploratory Dialogue within Synchronous Text Chat Proc 1st Int Conf Learning Analytics amp Knowledge Feb 27-Mar 1 2011 Banff ACM Press Eprint httporoopenacuk28955
Given a 25 hour webinar where in the live textchat were the most effective learning conversations Not at the start and end of a webinar but if we zoom in on a peakhellip
Discourse analytics on webinar textchat
-100
-50
0
50
100
92
8 9
40
9
50
1
000
1
007
1
017
1
031
1
045
1
104
1
117
1
126
1
132
1
138
1
144
1
152
1
203
Averag
Wei amp He extensions to Ferguson R and Buckingham Shum S (2011) Learning Analytics to Identify Exploratory Dialogue within Synchronous Text Chat Proc 1st Int Conf Learning Analytics amp Knowledge Feb 27-Mar 1 2011 Banff ACM Press Eprint httporoopenacuk28955
Classified as ldquoexploratory
talkrdquo
(more substantive for learning)
ldquonon-exploratoryrdquo
Given a 25 hour webinar where in the live textchat were the most effective learning conversations
De Liddo A Buckingham Shum S Quinto I Bachler M and Cannavacciuolo L Discourse-centric learning analytics 1st International Conference on Learning Analytics amp Knowledge (Banff 27 Mar-1 Apr 2011) httporoopenacuk25829
KMirsquos Cohere a web deliberation platform enabling semantic social network and discourse network analytics
Rebecca is playing the role of broker
connecting 2 peersrsquo contributions in meaningful ways
analytics for scholarly writing
42
Discourse analysis (Xerox Incremental Parser)
BACKGROUND KNOWLEDGE
Recent studies indicate hellip
hellip the previously proposed hellip
hellip is universally accepted
NOVELTY
new insights provide direct evidence
we suggest a new approach
results define a novel role
OPEN QUESTION hellip little is known hellip hellip role hellip has been elusive
Current data is insufficient hellip
GENERALIZING emerging as a promising approach Our understanding has grown exponentially growing recognition of the
importance
CONRASTING IDEAS hellip unorthodox view resolves hellip paradoxes hellip
In contrast with previous hypotheses
inconsistent with past findings
SIGNIFICANCE studies have provided important advances
Knowledge is crucial for understanding
valuable information from studies
SURPRISE We have recently observed surprisingly
We have identified unusual The recent discovery suggests intriguing roles
SUMMARIZING The goal of this study Here we show
Altogether our results indicate
Detection of salient sentences in scholarly reports based on the rhetorical signals authors use
Aacutegnes Saacutendor amp OLnet Project httpolnetorgnode512
De Liddo A Saacutendor Aacute and Buckingham Shum S Contested Collective Intelligence Rationale Technologies and a Human-Machine Annotation Study Computer Supported Cooperative Work 21 4-5 (2012) 417-448 httporoopenacuk31052
Human and machine analysis of a text for key contributions
19 sentences annotated 22 sentences annotated 11 sentences same as human annotation
71 sentences annotated 59 sentences annotated 42 sentences same as human annotation
Document 1
Document 2
httptechnologieskmiopenacukcohere20120109cohere-plus-automated-rhetorical-annotation De Liddo A Saacutendor Aacute and Buckingham Shum S Contested Collective Intelligence Rationale Technologies and a Human-Machine Annotation Study Computer Supported Cooperative Work 21 4-5 (2012) 417-448 httporoopenacuk31052
analytics for intepersonal networking
45
Semantic Social Network Analytics
De Liddo A Buckingham Shum S Quinto I Bachler M and Cannavacciuolo L Discourse-centric learning analytics 1st International Conference on Learning Analytics amp Knowledge (Banff 27 Mar-1 Apr 2011) httporoopenacuk25829
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Closing thoughts
54
55
ldquoThe basic question is not what can we measure
The basic question is
what does a good education look likerdquo
(Gardner Campbell)
httpchroniclecomblogstechtherapy20120502episode-95-learning-analytics-could-lead-to-wal-martification-of-college httplak12wikispacescomRecordings
56
Our analytics promote values pedagogy and assessment regimes
Are we clear which master
our analytics serve Are we happy to be judged by them
57
Will learning analytics merely turbocharge the current educational paradigm
mdash which is so often declared
not fit for purposehellip
58
hellipor will learning analytics reflect what we now know about designing authentic
engaged learning developing the new qualities that a
complex society demands
analytics grounded in the principles of good
assessment for learning
(not summative assessment for
grading pupils teachers institutions or nations)
27
Assessment for Learning
28
httpassessment-reform-grouporg Few learning analytics are
currently able to take o board the richness of this
original conception of assessment for learning
Assessment for Learning
29
httpassessment-reform-grouporg
Assessment for Learning
30
httpassessment-reform-grouporg
To what extent could automated
feedback be designed and evaluated with
emotional impact in mind
Assessment for Learning
31
httpassessment-reform-grouporg
Can analytics identify proxies
for such advanced qualities
Assessment for Learning
32
httpassessment-reform-grouporg
How do we provide analytics feedback
that does not disempower and de-motivate struggling
learners
analytics for learning conversations
33
Socio-cultural discourse analysis (Mercer et al OU)
bull Disputational talk characterised by disagreement and individualised decision making
bull Cumulative talk in which speakers build positively but uncritically on what the others have said
bull Exploratory talk in which partners engage critically but constructively with each others ideas
34 Mercer N (2004) Sociocultural discourse analysis analysing classroom talk as a social mode of thinking Journal of Applied Linguistics 1(2) 137-168
bull Exploratory talk in which partners engage critically but constructively with each others ideas
bull Statements and suggestions are offered for joint consideration
bull These may be challenged and counter-challenged but challenges are justified and alternative hypotheses are offered
bull Partners all actively participate and opinions are sought and considered before decisions are jointly made
bull Compared with the other two types in Exploratory talk knowledge is made more publicly accountable and reasoning is more visible in the talk
35
Socio-cultural discourse analysis (Mercer et al OU)
Mercer N (2004) Sociocultural discourse analysis analysing classroom talk as a social mode of thinking Journal of Applied Linguistics 1(2) 137-168
Analytics for identifying Exploratory talk
36
Elluminate sessions can be very long ndash lasting for hours or even covering days of a conference
It would be useful if we could identify where quality learning conversations seem to be taking place so we can recommend those sessions and not have to sit through online chat about virtual biscuits
Ferguson R and Buckingham Shum S Learning analytics to identify exploratory dialogue within synchronous text chat 1st International Conference on Learning Analytics amp Knowledge (Banff Canada 27 Mar-1 Apr 2011)
Defining indicators of Exploratory Talk
37
Category Indicator Challenge But if have to respond my view Critique However Irsquom not sure maybe Discussion of resources
Have you read more links
Evaluation Good example good point Explanation Means that our goals Explicit reasoning Next step relates to thatrsquos why Justification I mean we learned we observed Reflections of perspectives of others
Agree here is another makes the point take your point your view
Extract classified as Exploratory Talk
38
Time Contribution 242 PM I hate talking -P My question was whether gadgets were just
basically widgets and we could embed them in various web sites like Netvibes Google Desktop etc
242 PM Thanks thats great I am sure I understood everything but looks inspiring
243 PM Yes why OU tools not generic tools
243 PM Issues of interoperability
243 PM The new SocialLearn site looks a lot like a corkboard where you can add various widgets similar to those existing web start pages
243 PM What if we end up with as many appsgadgets as we have social networks and then we need a recommender for the apps
243 PM My question was on the definition of the crowd in the wisdom of crowds we acsess in the service model
243 PM there are various different flavours of widget eg Google gadgets W3C widgets etc SocialLearn has gone for Google gadgets
-60
-40
-20
0
20
40
60
80
92
8 9
32
93
6
94
0
94
1
94
6
95
0
95
3
95
6
10
00
10
05
10
07
10
07
10
09
10
13
10
17
10
23
10
27
10
31
10
35
10
40
10
45
10
52
10
55
11
04
11
08
11
11
11
17
11
20
11
24
11
26
11
28
11
31
11
32
11
35
11
36
11
38
11
39
11
41
11
44
11
46
11
48
11
52
11
54
12
00
12
03
12
04
12
05
Average Exploratory
Discourse analytics on webinar textchat
Sheffield UK not as sunny as yesterday - still warm Greetings from Hong Kong Morning from Wiltshire sunny here
See you bye for now bye and thank you Bye all for now
Extensions to Ferguson R and Buckingham Shum S (2011) Learning Analytics to Identify Exploratory Dialogue within Synchronous Text Chat Proc 1st Int Conf Learning Analytics amp Knowledge Feb 27-Mar 1 2011 Banff ACM Press Eprint httporoopenacuk28955
Given a 25 hour webinar where in the live textchat were the most effective learning conversations Not at the start and end of a webinar but if we zoom in on a peakhellip
Discourse analytics on webinar textchat
-100
-50
0
50
100
92
8 9
40
9
50
1
000
1
007
1
017
1
031
1
045
1
104
1
117
1
126
1
132
1
138
1
144
1
152
1
203
Averag
Wei amp He extensions to Ferguson R and Buckingham Shum S (2011) Learning Analytics to Identify Exploratory Dialogue within Synchronous Text Chat Proc 1st Int Conf Learning Analytics amp Knowledge Feb 27-Mar 1 2011 Banff ACM Press Eprint httporoopenacuk28955
Classified as ldquoexploratory
talkrdquo
(more substantive for learning)
ldquonon-exploratoryrdquo
Given a 25 hour webinar where in the live textchat were the most effective learning conversations
De Liddo A Buckingham Shum S Quinto I Bachler M and Cannavacciuolo L Discourse-centric learning analytics 1st International Conference on Learning Analytics amp Knowledge (Banff 27 Mar-1 Apr 2011) httporoopenacuk25829
KMirsquos Cohere a web deliberation platform enabling semantic social network and discourse network analytics
Rebecca is playing the role of broker
connecting 2 peersrsquo contributions in meaningful ways
analytics for scholarly writing
42
Discourse analysis (Xerox Incremental Parser)
BACKGROUND KNOWLEDGE
Recent studies indicate hellip
hellip the previously proposed hellip
hellip is universally accepted
NOVELTY
new insights provide direct evidence
we suggest a new approach
results define a novel role
OPEN QUESTION hellip little is known hellip hellip role hellip has been elusive
Current data is insufficient hellip
GENERALIZING emerging as a promising approach Our understanding has grown exponentially growing recognition of the
importance
CONRASTING IDEAS hellip unorthodox view resolves hellip paradoxes hellip
In contrast with previous hypotheses
inconsistent with past findings
SIGNIFICANCE studies have provided important advances
Knowledge is crucial for understanding
valuable information from studies
SURPRISE We have recently observed surprisingly
We have identified unusual The recent discovery suggests intriguing roles
SUMMARIZING The goal of this study Here we show
Altogether our results indicate
Detection of salient sentences in scholarly reports based on the rhetorical signals authors use
Aacutegnes Saacutendor amp OLnet Project httpolnetorgnode512
De Liddo A Saacutendor Aacute and Buckingham Shum S Contested Collective Intelligence Rationale Technologies and a Human-Machine Annotation Study Computer Supported Cooperative Work 21 4-5 (2012) 417-448 httporoopenacuk31052
Human and machine analysis of a text for key contributions
19 sentences annotated 22 sentences annotated 11 sentences same as human annotation
71 sentences annotated 59 sentences annotated 42 sentences same as human annotation
Document 1
Document 2
httptechnologieskmiopenacukcohere20120109cohere-plus-automated-rhetorical-annotation De Liddo A Saacutendor Aacute and Buckingham Shum S Contested Collective Intelligence Rationale Technologies and a Human-Machine Annotation Study Computer Supported Cooperative Work 21 4-5 (2012) 417-448 httporoopenacuk31052
analytics for intepersonal networking
45
Semantic Social Network Analytics
De Liddo A Buckingham Shum S Quinto I Bachler M and Cannavacciuolo L Discourse-centric learning analytics 1st International Conference on Learning Analytics amp Knowledge (Banff 27 Mar-1 Apr 2011) httporoopenacuk25829
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Closing thoughts
54
55
ldquoThe basic question is not what can we measure
The basic question is
what does a good education look likerdquo
(Gardner Campbell)
httpchroniclecomblogstechtherapy20120502episode-95-learning-analytics-could-lead-to-wal-martification-of-college httplak12wikispacescomRecordings
56
Our analytics promote values pedagogy and assessment regimes
Are we clear which master
our analytics serve Are we happy to be judged by them
57
Will learning analytics merely turbocharge the current educational paradigm
mdash which is so often declared
not fit for purposehellip
58
hellipor will learning analytics reflect what we now know about designing authentic
engaged learning developing the new qualities that a
complex society demands
Assessment for Learning
28
httpassessment-reform-grouporg Few learning analytics are
currently able to take o board the richness of this
original conception of assessment for learning
Assessment for Learning
29
httpassessment-reform-grouporg
Assessment for Learning
30
httpassessment-reform-grouporg
To what extent could automated
feedback be designed and evaluated with
emotional impact in mind
Assessment for Learning
31
httpassessment-reform-grouporg
Can analytics identify proxies
for such advanced qualities
Assessment for Learning
32
httpassessment-reform-grouporg
How do we provide analytics feedback
that does not disempower and de-motivate struggling
learners
analytics for learning conversations
33
Socio-cultural discourse analysis (Mercer et al OU)
bull Disputational talk characterised by disagreement and individualised decision making
bull Cumulative talk in which speakers build positively but uncritically on what the others have said
bull Exploratory talk in which partners engage critically but constructively with each others ideas
34 Mercer N (2004) Sociocultural discourse analysis analysing classroom talk as a social mode of thinking Journal of Applied Linguistics 1(2) 137-168
bull Exploratory talk in which partners engage critically but constructively with each others ideas
bull Statements and suggestions are offered for joint consideration
bull These may be challenged and counter-challenged but challenges are justified and alternative hypotheses are offered
bull Partners all actively participate and opinions are sought and considered before decisions are jointly made
bull Compared with the other two types in Exploratory talk knowledge is made more publicly accountable and reasoning is more visible in the talk
35
Socio-cultural discourse analysis (Mercer et al OU)
Mercer N (2004) Sociocultural discourse analysis analysing classroom talk as a social mode of thinking Journal of Applied Linguistics 1(2) 137-168
Analytics for identifying Exploratory talk
36
Elluminate sessions can be very long ndash lasting for hours or even covering days of a conference
It would be useful if we could identify where quality learning conversations seem to be taking place so we can recommend those sessions and not have to sit through online chat about virtual biscuits
Ferguson R and Buckingham Shum S Learning analytics to identify exploratory dialogue within synchronous text chat 1st International Conference on Learning Analytics amp Knowledge (Banff Canada 27 Mar-1 Apr 2011)
Defining indicators of Exploratory Talk
37
Category Indicator Challenge But if have to respond my view Critique However Irsquom not sure maybe Discussion of resources
Have you read more links
Evaluation Good example good point Explanation Means that our goals Explicit reasoning Next step relates to thatrsquos why Justification I mean we learned we observed Reflections of perspectives of others
Agree here is another makes the point take your point your view
Extract classified as Exploratory Talk
38
Time Contribution 242 PM I hate talking -P My question was whether gadgets were just
basically widgets and we could embed them in various web sites like Netvibes Google Desktop etc
242 PM Thanks thats great I am sure I understood everything but looks inspiring
243 PM Yes why OU tools not generic tools
243 PM Issues of interoperability
243 PM The new SocialLearn site looks a lot like a corkboard where you can add various widgets similar to those existing web start pages
243 PM What if we end up with as many appsgadgets as we have social networks and then we need a recommender for the apps
243 PM My question was on the definition of the crowd in the wisdom of crowds we acsess in the service model
243 PM there are various different flavours of widget eg Google gadgets W3C widgets etc SocialLearn has gone for Google gadgets
-60
-40
-20
0
20
40
60
80
92
8 9
32
93
6
94
0
94
1
94
6
95
0
95
3
95
6
10
00
10
05
10
07
10
07
10
09
10
13
10
17
10
23
10
27
10
31
10
35
10
40
10
45
10
52
10
55
11
04
11
08
11
11
11
17
11
20
11
24
11
26
11
28
11
31
11
32
11
35
11
36
11
38
11
39
11
41
11
44
11
46
11
48
11
52
11
54
12
00
12
03
12
04
12
05
Average Exploratory
Discourse analytics on webinar textchat
Sheffield UK not as sunny as yesterday - still warm Greetings from Hong Kong Morning from Wiltshire sunny here
See you bye for now bye and thank you Bye all for now
Extensions to Ferguson R and Buckingham Shum S (2011) Learning Analytics to Identify Exploratory Dialogue within Synchronous Text Chat Proc 1st Int Conf Learning Analytics amp Knowledge Feb 27-Mar 1 2011 Banff ACM Press Eprint httporoopenacuk28955
Given a 25 hour webinar where in the live textchat were the most effective learning conversations Not at the start and end of a webinar but if we zoom in on a peakhellip
Discourse analytics on webinar textchat
-100
-50
0
50
100
92
8 9
40
9
50
1
000
1
007
1
017
1
031
1
045
1
104
1
117
1
126
1
132
1
138
1
144
1
152
1
203
Averag
Wei amp He extensions to Ferguson R and Buckingham Shum S (2011) Learning Analytics to Identify Exploratory Dialogue within Synchronous Text Chat Proc 1st Int Conf Learning Analytics amp Knowledge Feb 27-Mar 1 2011 Banff ACM Press Eprint httporoopenacuk28955
Classified as ldquoexploratory
talkrdquo
(more substantive for learning)
ldquonon-exploratoryrdquo
Given a 25 hour webinar where in the live textchat were the most effective learning conversations
De Liddo A Buckingham Shum S Quinto I Bachler M and Cannavacciuolo L Discourse-centric learning analytics 1st International Conference on Learning Analytics amp Knowledge (Banff 27 Mar-1 Apr 2011) httporoopenacuk25829
KMirsquos Cohere a web deliberation platform enabling semantic social network and discourse network analytics
Rebecca is playing the role of broker
connecting 2 peersrsquo contributions in meaningful ways
analytics for scholarly writing
42
Discourse analysis (Xerox Incremental Parser)
BACKGROUND KNOWLEDGE
Recent studies indicate hellip
hellip the previously proposed hellip
hellip is universally accepted
NOVELTY
new insights provide direct evidence
we suggest a new approach
results define a novel role
OPEN QUESTION hellip little is known hellip hellip role hellip has been elusive
Current data is insufficient hellip
GENERALIZING emerging as a promising approach Our understanding has grown exponentially growing recognition of the
importance
CONRASTING IDEAS hellip unorthodox view resolves hellip paradoxes hellip
In contrast with previous hypotheses
inconsistent with past findings
SIGNIFICANCE studies have provided important advances
Knowledge is crucial for understanding
valuable information from studies
SURPRISE We have recently observed surprisingly
We have identified unusual The recent discovery suggests intriguing roles
SUMMARIZING The goal of this study Here we show
Altogether our results indicate
Detection of salient sentences in scholarly reports based on the rhetorical signals authors use
Aacutegnes Saacutendor amp OLnet Project httpolnetorgnode512
De Liddo A Saacutendor Aacute and Buckingham Shum S Contested Collective Intelligence Rationale Technologies and a Human-Machine Annotation Study Computer Supported Cooperative Work 21 4-5 (2012) 417-448 httporoopenacuk31052
Human and machine analysis of a text for key contributions
19 sentences annotated 22 sentences annotated 11 sentences same as human annotation
71 sentences annotated 59 sentences annotated 42 sentences same as human annotation
Document 1
Document 2
httptechnologieskmiopenacukcohere20120109cohere-plus-automated-rhetorical-annotation De Liddo A Saacutendor Aacute and Buckingham Shum S Contested Collective Intelligence Rationale Technologies and a Human-Machine Annotation Study Computer Supported Cooperative Work 21 4-5 (2012) 417-448 httporoopenacuk31052
analytics for intepersonal networking
45
Semantic Social Network Analytics
De Liddo A Buckingham Shum S Quinto I Bachler M and Cannavacciuolo L Discourse-centric learning analytics 1st International Conference on Learning Analytics amp Knowledge (Banff 27 Mar-1 Apr 2011) httporoopenacuk25829
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Closing thoughts
54
55
ldquoThe basic question is not what can we measure
The basic question is
what does a good education look likerdquo
(Gardner Campbell)
httpchroniclecomblogstechtherapy20120502episode-95-learning-analytics-could-lead-to-wal-martification-of-college httplak12wikispacescomRecordings
56
Our analytics promote values pedagogy and assessment regimes
Are we clear which master
our analytics serve Are we happy to be judged by them
57
Will learning analytics merely turbocharge the current educational paradigm
mdash which is so often declared
not fit for purposehellip
58
hellipor will learning analytics reflect what we now know about designing authentic
engaged learning developing the new qualities that a
complex society demands
Assessment for Learning
29
httpassessment-reform-grouporg
Assessment for Learning
30
httpassessment-reform-grouporg
To what extent could automated
feedback be designed and evaluated with
emotional impact in mind
Assessment for Learning
31
httpassessment-reform-grouporg
Can analytics identify proxies
for such advanced qualities
Assessment for Learning
32
httpassessment-reform-grouporg
How do we provide analytics feedback
that does not disempower and de-motivate struggling
learners
analytics for learning conversations
33
Socio-cultural discourse analysis (Mercer et al OU)
bull Disputational talk characterised by disagreement and individualised decision making
bull Cumulative talk in which speakers build positively but uncritically on what the others have said
bull Exploratory talk in which partners engage critically but constructively with each others ideas
34 Mercer N (2004) Sociocultural discourse analysis analysing classroom talk as a social mode of thinking Journal of Applied Linguistics 1(2) 137-168
bull Exploratory talk in which partners engage critically but constructively with each others ideas
bull Statements and suggestions are offered for joint consideration
bull These may be challenged and counter-challenged but challenges are justified and alternative hypotheses are offered
bull Partners all actively participate and opinions are sought and considered before decisions are jointly made
bull Compared with the other two types in Exploratory talk knowledge is made more publicly accountable and reasoning is more visible in the talk
35
Socio-cultural discourse analysis (Mercer et al OU)
Mercer N (2004) Sociocultural discourse analysis analysing classroom talk as a social mode of thinking Journal of Applied Linguistics 1(2) 137-168
Analytics for identifying Exploratory talk
36
Elluminate sessions can be very long ndash lasting for hours or even covering days of a conference
It would be useful if we could identify where quality learning conversations seem to be taking place so we can recommend those sessions and not have to sit through online chat about virtual biscuits
Ferguson R and Buckingham Shum S Learning analytics to identify exploratory dialogue within synchronous text chat 1st International Conference on Learning Analytics amp Knowledge (Banff Canada 27 Mar-1 Apr 2011)
Defining indicators of Exploratory Talk
37
Category Indicator Challenge But if have to respond my view Critique However Irsquom not sure maybe Discussion of resources
Have you read more links
Evaluation Good example good point Explanation Means that our goals Explicit reasoning Next step relates to thatrsquos why Justification I mean we learned we observed Reflections of perspectives of others
Agree here is another makes the point take your point your view
Extract classified as Exploratory Talk
38
Time Contribution 242 PM I hate talking -P My question was whether gadgets were just
basically widgets and we could embed them in various web sites like Netvibes Google Desktop etc
242 PM Thanks thats great I am sure I understood everything but looks inspiring
243 PM Yes why OU tools not generic tools
243 PM Issues of interoperability
243 PM The new SocialLearn site looks a lot like a corkboard where you can add various widgets similar to those existing web start pages
243 PM What if we end up with as many appsgadgets as we have social networks and then we need a recommender for the apps
243 PM My question was on the definition of the crowd in the wisdom of crowds we acsess in the service model
243 PM there are various different flavours of widget eg Google gadgets W3C widgets etc SocialLearn has gone for Google gadgets
-60
-40
-20
0
20
40
60
80
92
8 9
32
93
6
94
0
94
1
94
6
95
0
95
3
95
6
10
00
10
05
10
07
10
07
10
09
10
13
10
17
10
23
10
27
10
31
10
35
10
40
10
45
10
52
10
55
11
04
11
08
11
11
11
17
11
20
11
24
11
26
11
28
11
31
11
32
11
35
11
36
11
38
11
39
11
41
11
44
11
46
11
48
11
52
11
54
12
00
12
03
12
04
12
05
Average Exploratory
Discourse analytics on webinar textchat
Sheffield UK not as sunny as yesterday - still warm Greetings from Hong Kong Morning from Wiltshire sunny here
See you bye for now bye and thank you Bye all for now
Extensions to Ferguson R and Buckingham Shum S (2011) Learning Analytics to Identify Exploratory Dialogue within Synchronous Text Chat Proc 1st Int Conf Learning Analytics amp Knowledge Feb 27-Mar 1 2011 Banff ACM Press Eprint httporoopenacuk28955
Given a 25 hour webinar where in the live textchat were the most effective learning conversations Not at the start and end of a webinar but if we zoom in on a peakhellip
Discourse analytics on webinar textchat
-100
-50
0
50
100
92
8 9
40
9
50
1
000
1
007
1
017
1
031
1
045
1
104
1
117
1
126
1
132
1
138
1
144
1
152
1
203
Averag
Wei amp He extensions to Ferguson R and Buckingham Shum S (2011) Learning Analytics to Identify Exploratory Dialogue within Synchronous Text Chat Proc 1st Int Conf Learning Analytics amp Knowledge Feb 27-Mar 1 2011 Banff ACM Press Eprint httporoopenacuk28955
Classified as ldquoexploratory
talkrdquo
(more substantive for learning)
ldquonon-exploratoryrdquo
Given a 25 hour webinar where in the live textchat were the most effective learning conversations
De Liddo A Buckingham Shum S Quinto I Bachler M and Cannavacciuolo L Discourse-centric learning analytics 1st International Conference on Learning Analytics amp Knowledge (Banff 27 Mar-1 Apr 2011) httporoopenacuk25829
KMirsquos Cohere a web deliberation platform enabling semantic social network and discourse network analytics
Rebecca is playing the role of broker
connecting 2 peersrsquo contributions in meaningful ways
analytics for scholarly writing
42
Discourse analysis (Xerox Incremental Parser)
BACKGROUND KNOWLEDGE
Recent studies indicate hellip
hellip the previously proposed hellip
hellip is universally accepted
NOVELTY
new insights provide direct evidence
we suggest a new approach
results define a novel role
OPEN QUESTION hellip little is known hellip hellip role hellip has been elusive
Current data is insufficient hellip
GENERALIZING emerging as a promising approach Our understanding has grown exponentially growing recognition of the
importance
CONRASTING IDEAS hellip unorthodox view resolves hellip paradoxes hellip
In contrast with previous hypotheses
inconsistent with past findings
SIGNIFICANCE studies have provided important advances
Knowledge is crucial for understanding
valuable information from studies
SURPRISE We have recently observed surprisingly
We have identified unusual The recent discovery suggests intriguing roles
SUMMARIZING The goal of this study Here we show
Altogether our results indicate
Detection of salient sentences in scholarly reports based on the rhetorical signals authors use
Aacutegnes Saacutendor amp OLnet Project httpolnetorgnode512
De Liddo A Saacutendor Aacute and Buckingham Shum S Contested Collective Intelligence Rationale Technologies and a Human-Machine Annotation Study Computer Supported Cooperative Work 21 4-5 (2012) 417-448 httporoopenacuk31052
Human and machine analysis of a text for key contributions
19 sentences annotated 22 sentences annotated 11 sentences same as human annotation
71 sentences annotated 59 sentences annotated 42 sentences same as human annotation
Document 1
Document 2
httptechnologieskmiopenacukcohere20120109cohere-plus-automated-rhetorical-annotation De Liddo A Saacutendor Aacute and Buckingham Shum S Contested Collective Intelligence Rationale Technologies and a Human-Machine Annotation Study Computer Supported Cooperative Work 21 4-5 (2012) 417-448 httporoopenacuk31052
analytics for intepersonal networking
45
Semantic Social Network Analytics
De Liddo A Buckingham Shum S Quinto I Bachler M and Cannavacciuolo L Discourse-centric learning analytics 1st International Conference on Learning Analytics amp Knowledge (Banff 27 Mar-1 Apr 2011) httporoopenacuk25829
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Closing thoughts
54
55
ldquoThe basic question is not what can we measure
The basic question is
what does a good education look likerdquo
(Gardner Campbell)
httpchroniclecomblogstechtherapy20120502episode-95-learning-analytics-could-lead-to-wal-martification-of-college httplak12wikispacescomRecordings
56
Our analytics promote values pedagogy and assessment regimes
Are we clear which master
our analytics serve Are we happy to be judged by them
57
Will learning analytics merely turbocharge the current educational paradigm
mdash which is so often declared
not fit for purposehellip
58
hellipor will learning analytics reflect what we now know about designing authentic
engaged learning developing the new qualities that a
complex society demands
Assessment for Learning
30
httpassessment-reform-grouporg
To what extent could automated
feedback be designed and evaluated with
emotional impact in mind
Assessment for Learning
31
httpassessment-reform-grouporg
Can analytics identify proxies
for such advanced qualities
Assessment for Learning
32
httpassessment-reform-grouporg
How do we provide analytics feedback
that does not disempower and de-motivate struggling
learners
analytics for learning conversations
33
Socio-cultural discourse analysis (Mercer et al OU)
bull Disputational talk characterised by disagreement and individualised decision making
bull Cumulative talk in which speakers build positively but uncritically on what the others have said
bull Exploratory talk in which partners engage critically but constructively with each others ideas
34 Mercer N (2004) Sociocultural discourse analysis analysing classroom talk as a social mode of thinking Journal of Applied Linguistics 1(2) 137-168
bull Exploratory talk in which partners engage critically but constructively with each others ideas
bull Statements and suggestions are offered for joint consideration
bull These may be challenged and counter-challenged but challenges are justified and alternative hypotheses are offered
bull Partners all actively participate and opinions are sought and considered before decisions are jointly made
bull Compared with the other two types in Exploratory talk knowledge is made more publicly accountable and reasoning is more visible in the talk
35
Socio-cultural discourse analysis (Mercer et al OU)
Mercer N (2004) Sociocultural discourse analysis analysing classroom talk as a social mode of thinking Journal of Applied Linguistics 1(2) 137-168
Analytics for identifying Exploratory talk
36
Elluminate sessions can be very long ndash lasting for hours or even covering days of a conference
It would be useful if we could identify where quality learning conversations seem to be taking place so we can recommend those sessions and not have to sit through online chat about virtual biscuits
Ferguson R and Buckingham Shum S Learning analytics to identify exploratory dialogue within synchronous text chat 1st International Conference on Learning Analytics amp Knowledge (Banff Canada 27 Mar-1 Apr 2011)
Defining indicators of Exploratory Talk
37
Category Indicator Challenge But if have to respond my view Critique However Irsquom not sure maybe Discussion of resources
Have you read more links
Evaluation Good example good point Explanation Means that our goals Explicit reasoning Next step relates to thatrsquos why Justification I mean we learned we observed Reflections of perspectives of others
Agree here is another makes the point take your point your view
Extract classified as Exploratory Talk
38
Time Contribution 242 PM I hate talking -P My question was whether gadgets were just
basically widgets and we could embed them in various web sites like Netvibes Google Desktop etc
242 PM Thanks thats great I am sure I understood everything but looks inspiring
243 PM Yes why OU tools not generic tools
243 PM Issues of interoperability
243 PM The new SocialLearn site looks a lot like a corkboard where you can add various widgets similar to those existing web start pages
243 PM What if we end up with as many appsgadgets as we have social networks and then we need a recommender for the apps
243 PM My question was on the definition of the crowd in the wisdom of crowds we acsess in the service model
243 PM there are various different flavours of widget eg Google gadgets W3C widgets etc SocialLearn has gone for Google gadgets
-60
-40
-20
0
20
40
60
80
92
8 9
32
93
6
94
0
94
1
94
6
95
0
95
3
95
6
10
00
10
05
10
07
10
07
10
09
10
13
10
17
10
23
10
27
10
31
10
35
10
40
10
45
10
52
10
55
11
04
11
08
11
11
11
17
11
20
11
24
11
26
11
28
11
31
11
32
11
35
11
36
11
38
11
39
11
41
11
44
11
46
11
48
11
52
11
54
12
00
12
03
12
04
12
05
Average Exploratory
Discourse analytics on webinar textchat
Sheffield UK not as sunny as yesterday - still warm Greetings from Hong Kong Morning from Wiltshire sunny here
See you bye for now bye and thank you Bye all for now
Extensions to Ferguson R and Buckingham Shum S (2011) Learning Analytics to Identify Exploratory Dialogue within Synchronous Text Chat Proc 1st Int Conf Learning Analytics amp Knowledge Feb 27-Mar 1 2011 Banff ACM Press Eprint httporoopenacuk28955
Given a 25 hour webinar where in the live textchat were the most effective learning conversations Not at the start and end of a webinar but if we zoom in on a peakhellip
Discourse analytics on webinar textchat
-100
-50
0
50
100
92
8 9
40
9
50
1
000
1
007
1
017
1
031
1
045
1
104
1
117
1
126
1
132
1
138
1
144
1
152
1
203
Averag
Wei amp He extensions to Ferguson R and Buckingham Shum S (2011) Learning Analytics to Identify Exploratory Dialogue within Synchronous Text Chat Proc 1st Int Conf Learning Analytics amp Knowledge Feb 27-Mar 1 2011 Banff ACM Press Eprint httporoopenacuk28955
Classified as ldquoexploratory
talkrdquo
(more substantive for learning)
ldquonon-exploratoryrdquo
Given a 25 hour webinar where in the live textchat were the most effective learning conversations
De Liddo A Buckingham Shum S Quinto I Bachler M and Cannavacciuolo L Discourse-centric learning analytics 1st International Conference on Learning Analytics amp Knowledge (Banff 27 Mar-1 Apr 2011) httporoopenacuk25829
KMirsquos Cohere a web deliberation platform enabling semantic social network and discourse network analytics
Rebecca is playing the role of broker
connecting 2 peersrsquo contributions in meaningful ways
analytics for scholarly writing
42
Discourse analysis (Xerox Incremental Parser)
BACKGROUND KNOWLEDGE
Recent studies indicate hellip
hellip the previously proposed hellip
hellip is universally accepted
NOVELTY
new insights provide direct evidence
we suggest a new approach
results define a novel role
OPEN QUESTION hellip little is known hellip hellip role hellip has been elusive
Current data is insufficient hellip
GENERALIZING emerging as a promising approach Our understanding has grown exponentially growing recognition of the
importance
CONRASTING IDEAS hellip unorthodox view resolves hellip paradoxes hellip
In contrast with previous hypotheses
inconsistent with past findings
SIGNIFICANCE studies have provided important advances
Knowledge is crucial for understanding
valuable information from studies
SURPRISE We have recently observed surprisingly
We have identified unusual The recent discovery suggests intriguing roles
SUMMARIZING The goal of this study Here we show
Altogether our results indicate
Detection of salient sentences in scholarly reports based on the rhetorical signals authors use
Aacutegnes Saacutendor amp OLnet Project httpolnetorgnode512
De Liddo A Saacutendor Aacute and Buckingham Shum S Contested Collective Intelligence Rationale Technologies and a Human-Machine Annotation Study Computer Supported Cooperative Work 21 4-5 (2012) 417-448 httporoopenacuk31052
Human and machine analysis of a text for key contributions
19 sentences annotated 22 sentences annotated 11 sentences same as human annotation
71 sentences annotated 59 sentences annotated 42 sentences same as human annotation
Document 1
Document 2
httptechnologieskmiopenacukcohere20120109cohere-plus-automated-rhetorical-annotation De Liddo A Saacutendor Aacute and Buckingham Shum S Contested Collective Intelligence Rationale Technologies and a Human-Machine Annotation Study Computer Supported Cooperative Work 21 4-5 (2012) 417-448 httporoopenacuk31052
analytics for intepersonal networking
45
Semantic Social Network Analytics
De Liddo A Buckingham Shum S Quinto I Bachler M and Cannavacciuolo L Discourse-centric learning analytics 1st International Conference on Learning Analytics amp Knowledge (Banff 27 Mar-1 Apr 2011) httporoopenacuk25829
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Closing thoughts
54
55
ldquoThe basic question is not what can we measure
The basic question is
what does a good education look likerdquo
(Gardner Campbell)
httpchroniclecomblogstechtherapy20120502episode-95-learning-analytics-could-lead-to-wal-martification-of-college httplak12wikispacescomRecordings
56
Our analytics promote values pedagogy and assessment regimes
Are we clear which master
our analytics serve Are we happy to be judged by them
57
Will learning analytics merely turbocharge the current educational paradigm
mdash which is so often declared
not fit for purposehellip
58
hellipor will learning analytics reflect what we now know about designing authentic
engaged learning developing the new qualities that a
complex society demands
Assessment for Learning
31
httpassessment-reform-grouporg
Can analytics identify proxies
for such advanced qualities
Assessment for Learning
32
httpassessment-reform-grouporg
How do we provide analytics feedback
that does not disempower and de-motivate struggling
learners
analytics for learning conversations
33
Socio-cultural discourse analysis (Mercer et al OU)
bull Disputational talk characterised by disagreement and individualised decision making
bull Cumulative talk in which speakers build positively but uncritically on what the others have said
bull Exploratory talk in which partners engage critically but constructively with each others ideas
34 Mercer N (2004) Sociocultural discourse analysis analysing classroom talk as a social mode of thinking Journal of Applied Linguistics 1(2) 137-168
bull Exploratory talk in which partners engage critically but constructively with each others ideas
bull Statements and suggestions are offered for joint consideration
bull These may be challenged and counter-challenged but challenges are justified and alternative hypotheses are offered
bull Partners all actively participate and opinions are sought and considered before decisions are jointly made
bull Compared with the other two types in Exploratory talk knowledge is made more publicly accountable and reasoning is more visible in the talk
35
Socio-cultural discourse analysis (Mercer et al OU)
Mercer N (2004) Sociocultural discourse analysis analysing classroom talk as a social mode of thinking Journal of Applied Linguistics 1(2) 137-168
Analytics for identifying Exploratory talk
36
Elluminate sessions can be very long ndash lasting for hours or even covering days of a conference
It would be useful if we could identify where quality learning conversations seem to be taking place so we can recommend those sessions and not have to sit through online chat about virtual biscuits
Ferguson R and Buckingham Shum S Learning analytics to identify exploratory dialogue within synchronous text chat 1st International Conference on Learning Analytics amp Knowledge (Banff Canada 27 Mar-1 Apr 2011)
Defining indicators of Exploratory Talk
37
Category Indicator Challenge But if have to respond my view Critique However Irsquom not sure maybe Discussion of resources
Have you read more links
Evaluation Good example good point Explanation Means that our goals Explicit reasoning Next step relates to thatrsquos why Justification I mean we learned we observed Reflections of perspectives of others
Agree here is another makes the point take your point your view
Extract classified as Exploratory Talk
38
Time Contribution 242 PM I hate talking -P My question was whether gadgets were just
basically widgets and we could embed them in various web sites like Netvibes Google Desktop etc
242 PM Thanks thats great I am sure I understood everything but looks inspiring
243 PM Yes why OU tools not generic tools
243 PM Issues of interoperability
243 PM The new SocialLearn site looks a lot like a corkboard where you can add various widgets similar to those existing web start pages
243 PM What if we end up with as many appsgadgets as we have social networks and then we need a recommender for the apps
243 PM My question was on the definition of the crowd in the wisdom of crowds we acsess in the service model
243 PM there are various different flavours of widget eg Google gadgets W3C widgets etc SocialLearn has gone for Google gadgets
-60
-40
-20
0
20
40
60
80
92
8 9
32
93
6
94
0
94
1
94
6
95
0
95
3
95
6
10
00
10
05
10
07
10
07
10
09
10
13
10
17
10
23
10
27
10
31
10
35
10
40
10
45
10
52
10
55
11
04
11
08
11
11
11
17
11
20
11
24
11
26
11
28
11
31
11
32
11
35
11
36
11
38
11
39
11
41
11
44
11
46
11
48
11
52
11
54
12
00
12
03
12
04
12
05
Average Exploratory
Discourse analytics on webinar textchat
Sheffield UK not as sunny as yesterday - still warm Greetings from Hong Kong Morning from Wiltshire sunny here
See you bye for now bye and thank you Bye all for now
Extensions to Ferguson R and Buckingham Shum S (2011) Learning Analytics to Identify Exploratory Dialogue within Synchronous Text Chat Proc 1st Int Conf Learning Analytics amp Knowledge Feb 27-Mar 1 2011 Banff ACM Press Eprint httporoopenacuk28955
Given a 25 hour webinar where in the live textchat were the most effective learning conversations Not at the start and end of a webinar but if we zoom in on a peakhellip
Discourse analytics on webinar textchat
-100
-50
0
50
100
92
8 9
40
9
50
1
000
1
007
1
017
1
031
1
045
1
104
1
117
1
126
1
132
1
138
1
144
1
152
1
203
Averag
Wei amp He extensions to Ferguson R and Buckingham Shum S (2011) Learning Analytics to Identify Exploratory Dialogue within Synchronous Text Chat Proc 1st Int Conf Learning Analytics amp Knowledge Feb 27-Mar 1 2011 Banff ACM Press Eprint httporoopenacuk28955
Classified as ldquoexploratory
talkrdquo
(more substantive for learning)
ldquonon-exploratoryrdquo
Given a 25 hour webinar where in the live textchat were the most effective learning conversations
De Liddo A Buckingham Shum S Quinto I Bachler M and Cannavacciuolo L Discourse-centric learning analytics 1st International Conference on Learning Analytics amp Knowledge (Banff 27 Mar-1 Apr 2011) httporoopenacuk25829
KMirsquos Cohere a web deliberation platform enabling semantic social network and discourse network analytics
Rebecca is playing the role of broker
connecting 2 peersrsquo contributions in meaningful ways
analytics for scholarly writing
42
Discourse analysis (Xerox Incremental Parser)
BACKGROUND KNOWLEDGE
Recent studies indicate hellip
hellip the previously proposed hellip
hellip is universally accepted
NOVELTY
new insights provide direct evidence
we suggest a new approach
results define a novel role
OPEN QUESTION hellip little is known hellip hellip role hellip has been elusive
Current data is insufficient hellip
GENERALIZING emerging as a promising approach Our understanding has grown exponentially growing recognition of the
importance
CONRASTING IDEAS hellip unorthodox view resolves hellip paradoxes hellip
In contrast with previous hypotheses
inconsistent with past findings
SIGNIFICANCE studies have provided important advances
Knowledge is crucial for understanding
valuable information from studies
SURPRISE We have recently observed surprisingly
We have identified unusual The recent discovery suggests intriguing roles
SUMMARIZING The goal of this study Here we show
Altogether our results indicate
Detection of salient sentences in scholarly reports based on the rhetorical signals authors use
Aacutegnes Saacutendor amp OLnet Project httpolnetorgnode512
De Liddo A Saacutendor Aacute and Buckingham Shum S Contested Collective Intelligence Rationale Technologies and a Human-Machine Annotation Study Computer Supported Cooperative Work 21 4-5 (2012) 417-448 httporoopenacuk31052
Human and machine analysis of a text for key contributions
19 sentences annotated 22 sentences annotated 11 sentences same as human annotation
71 sentences annotated 59 sentences annotated 42 sentences same as human annotation
Document 1
Document 2
httptechnologieskmiopenacukcohere20120109cohere-plus-automated-rhetorical-annotation De Liddo A Saacutendor Aacute and Buckingham Shum S Contested Collective Intelligence Rationale Technologies and a Human-Machine Annotation Study Computer Supported Cooperative Work 21 4-5 (2012) 417-448 httporoopenacuk31052
analytics for intepersonal networking
45
Semantic Social Network Analytics
De Liddo A Buckingham Shum S Quinto I Bachler M and Cannavacciuolo L Discourse-centric learning analytics 1st International Conference on Learning Analytics amp Knowledge (Banff 27 Mar-1 Apr 2011) httporoopenacuk25829
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Closing thoughts
54
55
ldquoThe basic question is not what can we measure
The basic question is
what does a good education look likerdquo
(Gardner Campbell)
httpchroniclecomblogstechtherapy20120502episode-95-learning-analytics-could-lead-to-wal-martification-of-college httplak12wikispacescomRecordings
56
Our analytics promote values pedagogy and assessment regimes
Are we clear which master
our analytics serve Are we happy to be judged by them
57
Will learning analytics merely turbocharge the current educational paradigm
mdash which is so often declared
not fit for purposehellip
58
hellipor will learning analytics reflect what we now know about designing authentic
engaged learning developing the new qualities that a
complex society demands
Assessment for Learning
32
httpassessment-reform-grouporg
How do we provide analytics feedback
that does not disempower and de-motivate struggling
learners
analytics for learning conversations
33
Socio-cultural discourse analysis (Mercer et al OU)
bull Disputational talk characterised by disagreement and individualised decision making
bull Cumulative talk in which speakers build positively but uncritically on what the others have said
bull Exploratory talk in which partners engage critically but constructively with each others ideas
34 Mercer N (2004) Sociocultural discourse analysis analysing classroom talk as a social mode of thinking Journal of Applied Linguistics 1(2) 137-168
bull Exploratory talk in which partners engage critically but constructively with each others ideas
bull Statements and suggestions are offered for joint consideration
bull These may be challenged and counter-challenged but challenges are justified and alternative hypotheses are offered
bull Partners all actively participate and opinions are sought and considered before decisions are jointly made
bull Compared with the other two types in Exploratory talk knowledge is made more publicly accountable and reasoning is more visible in the talk
35
Socio-cultural discourse analysis (Mercer et al OU)
Mercer N (2004) Sociocultural discourse analysis analysing classroom talk as a social mode of thinking Journal of Applied Linguistics 1(2) 137-168
Analytics for identifying Exploratory talk
36
Elluminate sessions can be very long ndash lasting for hours or even covering days of a conference
It would be useful if we could identify where quality learning conversations seem to be taking place so we can recommend those sessions and not have to sit through online chat about virtual biscuits
Ferguson R and Buckingham Shum S Learning analytics to identify exploratory dialogue within synchronous text chat 1st International Conference on Learning Analytics amp Knowledge (Banff Canada 27 Mar-1 Apr 2011)
Defining indicators of Exploratory Talk
37
Category Indicator Challenge But if have to respond my view Critique However Irsquom not sure maybe Discussion of resources
Have you read more links
Evaluation Good example good point Explanation Means that our goals Explicit reasoning Next step relates to thatrsquos why Justification I mean we learned we observed Reflections of perspectives of others
Agree here is another makes the point take your point your view
Extract classified as Exploratory Talk
38
Time Contribution 242 PM I hate talking -P My question was whether gadgets were just
basically widgets and we could embed them in various web sites like Netvibes Google Desktop etc
242 PM Thanks thats great I am sure I understood everything but looks inspiring
243 PM Yes why OU tools not generic tools
243 PM Issues of interoperability
243 PM The new SocialLearn site looks a lot like a corkboard where you can add various widgets similar to those existing web start pages
243 PM What if we end up with as many appsgadgets as we have social networks and then we need a recommender for the apps
243 PM My question was on the definition of the crowd in the wisdom of crowds we acsess in the service model
243 PM there are various different flavours of widget eg Google gadgets W3C widgets etc SocialLearn has gone for Google gadgets
-60
-40
-20
0
20
40
60
80
92
8 9
32
93
6
94
0
94
1
94
6
95
0
95
3
95
6
10
00
10
05
10
07
10
07
10
09
10
13
10
17
10
23
10
27
10
31
10
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10
40
10
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10
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10
55
11
04
11
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11
11
11
17
11
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11
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11
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11
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11
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11
32
11
35
11
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11
38
11
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11
41
11
44
11
46
11
48
11
52
11
54
12
00
12
03
12
04
12
05
Average Exploratory
Discourse analytics on webinar textchat
Sheffield UK not as sunny as yesterday - still warm Greetings from Hong Kong Morning from Wiltshire sunny here
See you bye for now bye and thank you Bye all for now
Extensions to Ferguson R and Buckingham Shum S (2011) Learning Analytics to Identify Exploratory Dialogue within Synchronous Text Chat Proc 1st Int Conf Learning Analytics amp Knowledge Feb 27-Mar 1 2011 Banff ACM Press Eprint httporoopenacuk28955
Given a 25 hour webinar where in the live textchat were the most effective learning conversations Not at the start and end of a webinar but if we zoom in on a peakhellip
Discourse analytics on webinar textchat
-100
-50
0
50
100
92
8 9
40
9
50
1
000
1
007
1
017
1
031
1
045
1
104
1
117
1
126
1
132
1
138
1
144
1
152
1
203
Averag
Wei amp He extensions to Ferguson R and Buckingham Shum S (2011) Learning Analytics to Identify Exploratory Dialogue within Synchronous Text Chat Proc 1st Int Conf Learning Analytics amp Knowledge Feb 27-Mar 1 2011 Banff ACM Press Eprint httporoopenacuk28955
Classified as ldquoexploratory
talkrdquo
(more substantive for learning)
ldquonon-exploratoryrdquo
Given a 25 hour webinar where in the live textchat were the most effective learning conversations
De Liddo A Buckingham Shum S Quinto I Bachler M and Cannavacciuolo L Discourse-centric learning analytics 1st International Conference on Learning Analytics amp Knowledge (Banff 27 Mar-1 Apr 2011) httporoopenacuk25829
KMirsquos Cohere a web deliberation platform enabling semantic social network and discourse network analytics
Rebecca is playing the role of broker
connecting 2 peersrsquo contributions in meaningful ways
analytics for scholarly writing
42
Discourse analysis (Xerox Incremental Parser)
BACKGROUND KNOWLEDGE
Recent studies indicate hellip
hellip the previously proposed hellip
hellip is universally accepted
NOVELTY
new insights provide direct evidence
we suggest a new approach
results define a novel role
OPEN QUESTION hellip little is known hellip hellip role hellip has been elusive
Current data is insufficient hellip
GENERALIZING emerging as a promising approach Our understanding has grown exponentially growing recognition of the
importance
CONRASTING IDEAS hellip unorthodox view resolves hellip paradoxes hellip
In contrast with previous hypotheses
inconsistent with past findings
SIGNIFICANCE studies have provided important advances
Knowledge is crucial for understanding
valuable information from studies
SURPRISE We have recently observed surprisingly
We have identified unusual The recent discovery suggests intriguing roles
SUMMARIZING The goal of this study Here we show
Altogether our results indicate
Detection of salient sentences in scholarly reports based on the rhetorical signals authors use
Aacutegnes Saacutendor amp OLnet Project httpolnetorgnode512
De Liddo A Saacutendor Aacute and Buckingham Shum S Contested Collective Intelligence Rationale Technologies and a Human-Machine Annotation Study Computer Supported Cooperative Work 21 4-5 (2012) 417-448 httporoopenacuk31052
Human and machine analysis of a text for key contributions
19 sentences annotated 22 sentences annotated 11 sentences same as human annotation
71 sentences annotated 59 sentences annotated 42 sentences same as human annotation
Document 1
Document 2
httptechnologieskmiopenacukcohere20120109cohere-plus-automated-rhetorical-annotation De Liddo A Saacutendor Aacute and Buckingham Shum S Contested Collective Intelligence Rationale Technologies and a Human-Machine Annotation Study Computer Supported Cooperative Work 21 4-5 (2012) 417-448 httporoopenacuk31052
analytics for intepersonal networking
45
Semantic Social Network Analytics
De Liddo A Buckingham Shum S Quinto I Bachler M and Cannavacciuolo L Discourse-centric learning analytics 1st International Conference on Learning Analytics amp Knowledge (Banff 27 Mar-1 Apr 2011) httporoopenacuk25829
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Closing thoughts
54
55
ldquoThe basic question is not what can we measure
The basic question is
what does a good education look likerdquo
(Gardner Campbell)
httpchroniclecomblogstechtherapy20120502episode-95-learning-analytics-could-lead-to-wal-martification-of-college httplak12wikispacescomRecordings
56
Our analytics promote values pedagogy and assessment regimes
Are we clear which master
our analytics serve Are we happy to be judged by them
57
Will learning analytics merely turbocharge the current educational paradigm
mdash which is so often declared
not fit for purposehellip
58
hellipor will learning analytics reflect what we now know about designing authentic
engaged learning developing the new qualities that a
complex society demands
analytics for learning conversations
33
Socio-cultural discourse analysis (Mercer et al OU)
bull Disputational talk characterised by disagreement and individualised decision making
bull Cumulative talk in which speakers build positively but uncritically on what the others have said
bull Exploratory talk in which partners engage critically but constructively with each others ideas
34 Mercer N (2004) Sociocultural discourse analysis analysing classroom talk as a social mode of thinking Journal of Applied Linguistics 1(2) 137-168
bull Exploratory talk in which partners engage critically but constructively with each others ideas
bull Statements and suggestions are offered for joint consideration
bull These may be challenged and counter-challenged but challenges are justified and alternative hypotheses are offered
bull Partners all actively participate and opinions are sought and considered before decisions are jointly made
bull Compared with the other two types in Exploratory talk knowledge is made more publicly accountable and reasoning is more visible in the talk
35
Socio-cultural discourse analysis (Mercer et al OU)
Mercer N (2004) Sociocultural discourse analysis analysing classroom talk as a social mode of thinking Journal of Applied Linguistics 1(2) 137-168
Analytics for identifying Exploratory talk
36
Elluminate sessions can be very long ndash lasting for hours or even covering days of a conference
It would be useful if we could identify where quality learning conversations seem to be taking place so we can recommend those sessions and not have to sit through online chat about virtual biscuits
Ferguson R and Buckingham Shum S Learning analytics to identify exploratory dialogue within synchronous text chat 1st International Conference on Learning Analytics amp Knowledge (Banff Canada 27 Mar-1 Apr 2011)
Defining indicators of Exploratory Talk
37
Category Indicator Challenge But if have to respond my view Critique However Irsquom not sure maybe Discussion of resources
Have you read more links
Evaluation Good example good point Explanation Means that our goals Explicit reasoning Next step relates to thatrsquos why Justification I mean we learned we observed Reflections of perspectives of others
Agree here is another makes the point take your point your view
Extract classified as Exploratory Talk
38
Time Contribution 242 PM I hate talking -P My question was whether gadgets were just
basically widgets and we could embed them in various web sites like Netvibes Google Desktop etc
242 PM Thanks thats great I am sure I understood everything but looks inspiring
243 PM Yes why OU tools not generic tools
243 PM Issues of interoperability
243 PM The new SocialLearn site looks a lot like a corkboard where you can add various widgets similar to those existing web start pages
243 PM What if we end up with as many appsgadgets as we have social networks and then we need a recommender for the apps
243 PM My question was on the definition of the crowd in the wisdom of crowds we acsess in the service model
243 PM there are various different flavours of widget eg Google gadgets W3C widgets etc SocialLearn has gone for Google gadgets
-60
-40
-20
0
20
40
60
80
92
8 9
32
93
6
94
0
94
1
94
6
95
0
95
3
95
6
10
00
10
05
10
07
10
07
10
09
10
13
10
17
10
23
10
27
10
31
10
35
10
40
10
45
10
52
10
55
11
04
11
08
11
11
11
17
11
20
11
24
11
26
11
28
11
31
11
32
11
35
11
36
11
38
11
39
11
41
11
44
11
46
11
48
11
52
11
54
12
00
12
03
12
04
12
05
Average Exploratory
Discourse analytics on webinar textchat
Sheffield UK not as sunny as yesterday - still warm Greetings from Hong Kong Morning from Wiltshire sunny here
See you bye for now bye and thank you Bye all for now
Extensions to Ferguson R and Buckingham Shum S (2011) Learning Analytics to Identify Exploratory Dialogue within Synchronous Text Chat Proc 1st Int Conf Learning Analytics amp Knowledge Feb 27-Mar 1 2011 Banff ACM Press Eprint httporoopenacuk28955
Given a 25 hour webinar where in the live textchat were the most effective learning conversations Not at the start and end of a webinar but if we zoom in on a peakhellip
Discourse analytics on webinar textchat
-100
-50
0
50
100
92
8 9
40
9
50
1
000
1
007
1
017
1
031
1
045
1
104
1
117
1
126
1
132
1
138
1
144
1
152
1
203
Averag
Wei amp He extensions to Ferguson R and Buckingham Shum S (2011) Learning Analytics to Identify Exploratory Dialogue within Synchronous Text Chat Proc 1st Int Conf Learning Analytics amp Knowledge Feb 27-Mar 1 2011 Banff ACM Press Eprint httporoopenacuk28955
Classified as ldquoexploratory
talkrdquo
(more substantive for learning)
ldquonon-exploratoryrdquo
Given a 25 hour webinar where in the live textchat were the most effective learning conversations
De Liddo A Buckingham Shum S Quinto I Bachler M and Cannavacciuolo L Discourse-centric learning analytics 1st International Conference on Learning Analytics amp Knowledge (Banff 27 Mar-1 Apr 2011) httporoopenacuk25829
KMirsquos Cohere a web deliberation platform enabling semantic social network and discourse network analytics
Rebecca is playing the role of broker
connecting 2 peersrsquo contributions in meaningful ways
analytics for scholarly writing
42
Discourse analysis (Xerox Incremental Parser)
BACKGROUND KNOWLEDGE
Recent studies indicate hellip
hellip the previously proposed hellip
hellip is universally accepted
NOVELTY
new insights provide direct evidence
we suggest a new approach
results define a novel role
OPEN QUESTION hellip little is known hellip hellip role hellip has been elusive
Current data is insufficient hellip
GENERALIZING emerging as a promising approach Our understanding has grown exponentially growing recognition of the
importance
CONRASTING IDEAS hellip unorthodox view resolves hellip paradoxes hellip
In contrast with previous hypotheses
inconsistent with past findings
SIGNIFICANCE studies have provided important advances
Knowledge is crucial for understanding
valuable information from studies
SURPRISE We have recently observed surprisingly
We have identified unusual The recent discovery suggests intriguing roles
SUMMARIZING The goal of this study Here we show
Altogether our results indicate
Detection of salient sentences in scholarly reports based on the rhetorical signals authors use
Aacutegnes Saacutendor amp OLnet Project httpolnetorgnode512
De Liddo A Saacutendor Aacute and Buckingham Shum S Contested Collective Intelligence Rationale Technologies and a Human-Machine Annotation Study Computer Supported Cooperative Work 21 4-5 (2012) 417-448 httporoopenacuk31052
Human and machine analysis of a text for key contributions
19 sentences annotated 22 sentences annotated 11 sentences same as human annotation
71 sentences annotated 59 sentences annotated 42 sentences same as human annotation
Document 1
Document 2
httptechnologieskmiopenacukcohere20120109cohere-plus-automated-rhetorical-annotation De Liddo A Saacutendor Aacute and Buckingham Shum S Contested Collective Intelligence Rationale Technologies and a Human-Machine Annotation Study Computer Supported Cooperative Work 21 4-5 (2012) 417-448 httporoopenacuk31052
analytics for intepersonal networking
45
Semantic Social Network Analytics
De Liddo A Buckingham Shum S Quinto I Bachler M and Cannavacciuolo L Discourse-centric learning analytics 1st International Conference on Learning Analytics amp Knowledge (Banff 27 Mar-1 Apr 2011) httporoopenacuk25829
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Closing thoughts
54
55
ldquoThe basic question is not what can we measure
The basic question is
what does a good education look likerdquo
(Gardner Campbell)
httpchroniclecomblogstechtherapy20120502episode-95-learning-analytics-could-lead-to-wal-martification-of-college httplak12wikispacescomRecordings
56
Our analytics promote values pedagogy and assessment regimes
Are we clear which master
our analytics serve Are we happy to be judged by them
57
Will learning analytics merely turbocharge the current educational paradigm
mdash which is so often declared
not fit for purposehellip
58
hellipor will learning analytics reflect what we now know about designing authentic
engaged learning developing the new qualities that a
complex society demands
Socio-cultural discourse analysis (Mercer et al OU)
bull Disputational talk characterised by disagreement and individualised decision making
bull Cumulative talk in which speakers build positively but uncritically on what the others have said
bull Exploratory talk in which partners engage critically but constructively with each others ideas
34 Mercer N (2004) Sociocultural discourse analysis analysing classroom talk as a social mode of thinking Journal of Applied Linguistics 1(2) 137-168
bull Exploratory talk in which partners engage critically but constructively with each others ideas
bull Statements and suggestions are offered for joint consideration
bull These may be challenged and counter-challenged but challenges are justified and alternative hypotheses are offered
bull Partners all actively participate and opinions are sought and considered before decisions are jointly made
bull Compared with the other two types in Exploratory talk knowledge is made more publicly accountable and reasoning is more visible in the talk
35
Socio-cultural discourse analysis (Mercer et al OU)
Mercer N (2004) Sociocultural discourse analysis analysing classroom talk as a social mode of thinking Journal of Applied Linguistics 1(2) 137-168
Analytics for identifying Exploratory talk
36
Elluminate sessions can be very long ndash lasting for hours or even covering days of a conference
It would be useful if we could identify where quality learning conversations seem to be taking place so we can recommend those sessions and not have to sit through online chat about virtual biscuits
Ferguson R and Buckingham Shum S Learning analytics to identify exploratory dialogue within synchronous text chat 1st International Conference on Learning Analytics amp Knowledge (Banff Canada 27 Mar-1 Apr 2011)
Defining indicators of Exploratory Talk
37
Category Indicator Challenge But if have to respond my view Critique However Irsquom not sure maybe Discussion of resources
Have you read more links
Evaluation Good example good point Explanation Means that our goals Explicit reasoning Next step relates to thatrsquos why Justification I mean we learned we observed Reflections of perspectives of others
Agree here is another makes the point take your point your view
Extract classified as Exploratory Talk
38
Time Contribution 242 PM I hate talking -P My question was whether gadgets were just
basically widgets and we could embed them in various web sites like Netvibes Google Desktop etc
242 PM Thanks thats great I am sure I understood everything but looks inspiring
243 PM Yes why OU tools not generic tools
243 PM Issues of interoperability
243 PM The new SocialLearn site looks a lot like a corkboard where you can add various widgets similar to those existing web start pages
243 PM What if we end up with as many appsgadgets as we have social networks and then we need a recommender for the apps
243 PM My question was on the definition of the crowd in the wisdom of crowds we acsess in the service model
243 PM there are various different flavours of widget eg Google gadgets W3C widgets etc SocialLearn has gone for Google gadgets
-60
-40
-20
0
20
40
60
80
92
8 9
32
93
6
94
0
94
1
94
6
95
0
95
3
95
6
10
00
10
05
10
07
10
07
10
09
10
13
10
17
10
23
10
27
10
31
10
35
10
40
10
45
10
52
10
55
11
04
11
08
11
11
11
17
11
20
11
24
11
26
11
28
11
31
11
32
11
35
11
36
11
38
11
39
11
41
11
44
11
46
11
48
11
52
11
54
12
00
12
03
12
04
12
05
Average Exploratory
Discourse analytics on webinar textchat
Sheffield UK not as sunny as yesterday - still warm Greetings from Hong Kong Morning from Wiltshire sunny here
See you bye for now bye and thank you Bye all for now
Extensions to Ferguson R and Buckingham Shum S (2011) Learning Analytics to Identify Exploratory Dialogue within Synchronous Text Chat Proc 1st Int Conf Learning Analytics amp Knowledge Feb 27-Mar 1 2011 Banff ACM Press Eprint httporoopenacuk28955
Given a 25 hour webinar where in the live textchat were the most effective learning conversations Not at the start and end of a webinar but if we zoom in on a peakhellip
Discourse analytics on webinar textchat
-100
-50
0
50
100
92
8 9
40
9
50
1
000
1
007
1
017
1
031
1
045
1
104
1
117
1
126
1
132
1
138
1
144
1
152
1
203
Averag
Wei amp He extensions to Ferguson R and Buckingham Shum S (2011) Learning Analytics to Identify Exploratory Dialogue within Synchronous Text Chat Proc 1st Int Conf Learning Analytics amp Knowledge Feb 27-Mar 1 2011 Banff ACM Press Eprint httporoopenacuk28955
Classified as ldquoexploratory
talkrdquo
(more substantive for learning)
ldquonon-exploratoryrdquo
Given a 25 hour webinar where in the live textchat were the most effective learning conversations
De Liddo A Buckingham Shum S Quinto I Bachler M and Cannavacciuolo L Discourse-centric learning analytics 1st International Conference on Learning Analytics amp Knowledge (Banff 27 Mar-1 Apr 2011) httporoopenacuk25829
KMirsquos Cohere a web deliberation platform enabling semantic social network and discourse network analytics
Rebecca is playing the role of broker
connecting 2 peersrsquo contributions in meaningful ways
analytics for scholarly writing
42
Discourse analysis (Xerox Incremental Parser)
BACKGROUND KNOWLEDGE
Recent studies indicate hellip
hellip the previously proposed hellip
hellip is universally accepted
NOVELTY
new insights provide direct evidence
we suggest a new approach
results define a novel role
OPEN QUESTION hellip little is known hellip hellip role hellip has been elusive
Current data is insufficient hellip
GENERALIZING emerging as a promising approach Our understanding has grown exponentially growing recognition of the
importance
CONRASTING IDEAS hellip unorthodox view resolves hellip paradoxes hellip
In contrast with previous hypotheses
inconsistent with past findings
SIGNIFICANCE studies have provided important advances
Knowledge is crucial for understanding
valuable information from studies
SURPRISE We have recently observed surprisingly
We have identified unusual The recent discovery suggests intriguing roles
SUMMARIZING The goal of this study Here we show
Altogether our results indicate
Detection of salient sentences in scholarly reports based on the rhetorical signals authors use
Aacutegnes Saacutendor amp OLnet Project httpolnetorgnode512
De Liddo A Saacutendor Aacute and Buckingham Shum S Contested Collective Intelligence Rationale Technologies and a Human-Machine Annotation Study Computer Supported Cooperative Work 21 4-5 (2012) 417-448 httporoopenacuk31052
Human and machine analysis of a text for key contributions
19 sentences annotated 22 sentences annotated 11 sentences same as human annotation
71 sentences annotated 59 sentences annotated 42 sentences same as human annotation
Document 1
Document 2
httptechnologieskmiopenacukcohere20120109cohere-plus-automated-rhetorical-annotation De Liddo A Saacutendor Aacute and Buckingham Shum S Contested Collective Intelligence Rationale Technologies and a Human-Machine Annotation Study Computer Supported Cooperative Work 21 4-5 (2012) 417-448 httporoopenacuk31052
analytics for intepersonal networking
45
Semantic Social Network Analytics
De Liddo A Buckingham Shum S Quinto I Bachler M and Cannavacciuolo L Discourse-centric learning analytics 1st International Conference on Learning Analytics amp Knowledge (Banff 27 Mar-1 Apr 2011) httporoopenacuk25829
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Closing thoughts
54
55
ldquoThe basic question is not what can we measure
The basic question is
what does a good education look likerdquo
(Gardner Campbell)
httpchroniclecomblogstechtherapy20120502episode-95-learning-analytics-could-lead-to-wal-martification-of-college httplak12wikispacescomRecordings
56
Our analytics promote values pedagogy and assessment regimes
Are we clear which master
our analytics serve Are we happy to be judged by them
57
Will learning analytics merely turbocharge the current educational paradigm
mdash which is so often declared
not fit for purposehellip
58
hellipor will learning analytics reflect what we now know about designing authentic
engaged learning developing the new qualities that a
complex society demands
bull Exploratory talk in which partners engage critically but constructively with each others ideas
bull Statements and suggestions are offered for joint consideration
bull These may be challenged and counter-challenged but challenges are justified and alternative hypotheses are offered
bull Partners all actively participate and opinions are sought and considered before decisions are jointly made
bull Compared with the other two types in Exploratory talk knowledge is made more publicly accountable and reasoning is more visible in the talk
35
Socio-cultural discourse analysis (Mercer et al OU)
Mercer N (2004) Sociocultural discourse analysis analysing classroom talk as a social mode of thinking Journal of Applied Linguistics 1(2) 137-168
Analytics for identifying Exploratory talk
36
Elluminate sessions can be very long ndash lasting for hours or even covering days of a conference
It would be useful if we could identify where quality learning conversations seem to be taking place so we can recommend those sessions and not have to sit through online chat about virtual biscuits
Ferguson R and Buckingham Shum S Learning analytics to identify exploratory dialogue within synchronous text chat 1st International Conference on Learning Analytics amp Knowledge (Banff Canada 27 Mar-1 Apr 2011)
Defining indicators of Exploratory Talk
37
Category Indicator Challenge But if have to respond my view Critique However Irsquom not sure maybe Discussion of resources
Have you read more links
Evaluation Good example good point Explanation Means that our goals Explicit reasoning Next step relates to thatrsquos why Justification I mean we learned we observed Reflections of perspectives of others
Agree here is another makes the point take your point your view
Extract classified as Exploratory Talk
38
Time Contribution 242 PM I hate talking -P My question was whether gadgets were just
basically widgets and we could embed them in various web sites like Netvibes Google Desktop etc
242 PM Thanks thats great I am sure I understood everything but looks inspiring
243 PM Yes why OU tools not generic tools
243 PM Issues of interoperability
243 PM The new SocialLearn site looks a lot like a corkboard where you can add various widgets similar to those existing web start pages
243 PM What if we end up with as many appsgadgets as we have social networks and then we need a recommender for the apps
243 PM My question was on the definition of the crowd in the wisdom of crowds we acsess in the service model
243 PM there are various different flavours of widget eg Google gadgets W3C widgets etc SocialLearn has gone for Google gadgets
-60
-40
-20
0
20
40
60
80
92
8 9
32
93
6
94
0
94
1
94
6
95
0
95
3
95
6
10
00
10
05
10
07
10
07
10
09
10
13
10
17
10
23
10
27
10
31
10
35
10
40
10
45
10
52
10
55
11
04
11
08
11
11
11
17
11
20
11
24
11
26
11
28
11
31
11
32
11
35
11
36
11
38
11
39
11
41
11
44
11
46
11
48
11
52
11
54
12
00
12
03
12
04
12
05
Average Exploratory
Discourse analytics on webinar textchat
Sheffield UK not as sunny as yesterday - still warm Greetings from Hong Kong Morning from Wiltshire sunny here
See you bye for now bye and thank you Bye all for now
Extensions to Ferguson R and Buckingham Shum S (2011) Learning Analytics to Identify Exploratory Dialogue within Synchronous Text Chat Proc 1st Int Conf Learning Analytics amp Knowledge Feb 27-Mar 1 2011 Banff ACM Press Eprint httporoopenacuk28955
Given a 25 hour webinar where in the live textchat were the most effective learning conversations Not at the start and end of a webinar but if we zoom in on a peakhellip
Discourse analytics on webinar textchat
-100
-50
0
50
100
92
8 9
40
9
50
1
000
1
007
1
017
1
031
1
045
1
104
1
117
1
126
1
132
1
138
1
144
1
152
1
203
Averag
Wei amp He extensions to Ferguson R and Buckingham Shum S (2011) Learning Analytics to Identify Exploratory Dialogue within Synchronous Text Chat Proc 1st Int Conf Learning Analytics amp Knowledge Feb 27-Mar 1 2011 Banff ACM Press Eprint httporoopenacuk28955
Classified as ldquoexploratory
talkrdquo
(more substantive for learning)
ldquonon-exploratoryrdquo
Given a 25 hour webinar where in the live textchat were the most effective learning conversations
De Liddo A Buckingham Shum S Quinto I Bachler M and Cannavacciuolo L Discourse-centric learning analytics 1st International Conference on Learning Analytics amp Knowledge (Banff 27 Mar-1 Apr 2011) httporoopenacuk25829
KMirsquos Cohere a web deliberation platform enabling semantic social network and discourse network analytics
Rebecca is playing the role of broker
connecting 2 peersrsquo contributions in meaningful ways
analytics for scholarly writing
42
Discourse analysis (Xerox Incremental Parser)
BACKGROUND KNOWLEDGE
Recent studies indicate hellip
hellip the previously proposed hellip
hellip is universally accepted
NOVELTY
new insights provide direct evidence
we suggest a new approach
results define a novel role
OPEN QUESTION hellip little is known hellip hellip role hellip has been elusive
Current data is insufficient hellip
GENERALIZING emerging as a promising approach Our understanding has grown exponentially growing recognition of the
importance
CONRASTING IDEAS hellip unorthodox view resolves hellip paradoxes hellip
In contrast with previous hypotheses
inconsistent with past findings
SIGNIFICANCE studies have provided important advances
Knowledge is crucial for understanding
valuable information from studies
SURPRISE We have recently observed surprisingly
We have identified unusual The recent discovery suggests intriguing roles
SUMMARIZING The goal of this study Here we show
Altogether our results indicate
Detection of salient sentences in scholarly reports based on the rhetorical signals authors use
Aacutegnes Saacutendor amp OLnet Project httpolnetorgnode512
De Liddo A Saacutendor Aacute and Buckingham Shum S Contested Collective Intelligence Rationale Technologies and a Human-Machine Annotation Study Computer Supported Cooperative Work 21 4-5 (2012) 417-448 httporoopenacuk31052
Human and machine analysis of a text for key contributions
19 sentences annotated 22 sentences annotated 11 sentences same as human annotation
71 sentences annotated 59 sentences annotated 42 sentences same as human annotation
Document 1
Document 2
httptechnologieskmiopenacukcohere20120109cohere-plus-automated-rhetorical-annotation De Liddo A Saacutendor Aacute and Buckingham Shum S Contested Collective Intelligence Rationale Technologies and a Human-Machine Annotation Study Computer Supported Cooperative Work 21 4-5 (2012) 417-448 httporoopenacuk31052
analytics for intepersonal networking
45
Semantic Social Network Analytics
De Liddo A Buckingham Shum S Quinto I Bachler M and Cannavacciuolo L Discourse-centric learning analytics 1st International Conference on Learning Analytics amp Knowledge (Banff 27 Mar-1 Apr 2011) httporoopenacuk25829
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Closing thoughts
54
55
ldquoThe basic question is not what can we measure
The basic question is
what does a good education look likerdquo
(Gardner Campbell)
httpchroniclecomblogstechtherapy20120502episode-95-learning-analytics-could-lead-to-wal-martification-of-college httplak12wikispacescomRecordings
56
Our analytics promote values pedagogy and assessment regimes
Are we clear which master
our analytics serve Are we happy to be judged by them
57
Will learning analytics merely turbocharge the current educational paradigm
mdash which is so often declared
not fit for purposehellip
58
hellipor will learning analytics reflect what we now know about designing authentic
engaged learning developing the new qualities that a
complex society demands
Analytics for identifying Exploratory talk
36
Elluminate sessions can be very long ndash lasting for hours or even covering days of a conference
It would be useful if we could identify where quality learning conversations seem to be taking place so we can recommend those sessions and not have to sit through online chat about virtual biscuits
Ferguson R and Buckingham Shum S Learning analytics to identify exploratory dialogue within synchronous text chat 1st International Conference on Learning Analytics amp Knowledge (Banff Canada 27 Mar-1 Apr 2011)
Defining indicators of Exploratory Talk
37
Category Indicator Challenge But if have to respond my view Critique However Irsquom not sure maybe Discussion of resources
Have you read more links
Evaluation Good example good point Explanation Means that our goals Explicit reasoning Next step relates to thatrsquos why Justification I mean we learned we observed Reflections of perspectives of others
Agree here is another makes the point take your point your view
Extract classified as Exploratory Talk
38
Time Contribution 242 PM I hate talking -P My question was whether gadgets were just
basically widgets and we could embed them in various web sites like Netvibes Google Desktop etc
242 PM Thanks thats great I am sure I understood everything but looks inspiring
243 PM Yes why OU tools not generic tools
243 PM Issues of interoperability
243 PM The new SocialLearn site looks a lot like a corkboard where you can add various widgets similar to those existing web start pages
243 PM What if we end up with as many appsgadgets as we have social networks and then we need a recommender for the apps
243 PM My question was on the definition of the crowd in the wisdom of crowds we acsess in the service model
243 PM there are various different flavours of widget eg Google gadgets W3C widgets etc SocialLearn has gone for Google gadgets
-60
-40
-20
0
20
40
60
80
92
8 9
32
93
6
94
0
94
1
94
6
95
0
95
3
95
6
10
00
10
05
10
07
10
07
10
09
10
13
10
17
10
23
10
27
10
31
10
35
10
40
10
45
10
52
10
55
11
04
11
08
11
11
11
17
11
20
11
24
11
26
11
28
11
31
11
32
11
35
11
36
11
38
11
39
11
41
11
44
11
46
11
48
11
52
11
54
12
00
12
03
12
04
12
05
Average Exploratory
Discourse analytics on webinar textchat
Sheffield UK not as sunny as yesterday - still warm Greetings from Hong Kong Morning from Wiltshire sunny here
See you bye for now bye and thank you Bye all for now
Extensions to Ferguson R and Buckingham Shum S (2011) Learning Analytics to Identify Exploratory Dialogue within Synchronous Text Chat Proc 1st Int Conf Learning Analytics amp Knowledge Feb 27-Mar 1 2011 Banff ACM Press Eprint httporoopenacuk28955
Given a 25 hour webinar where in the live textchat were the most effective learning conversations Not at the start and end of a webinar but if we zoom in on a peakhellip
Discourse analytics on webinar textchat
-100
-50
0
50
100
92
8 9
40
9
50
1
000
1
007
1
017
1
031
1
045
1
104
1
117
1
126
1
132
1
138
1
144
1
152
1
203
Averag
Wei amp He extensions to Ferguson R and Buckingham Shum S (2011) Learning Analytics to Identify Exploratory Dialogue within Synchronous Text Chat Proc 1st Int Conf Learning Analytics amp Knowledge Feb 27-Mar 1 2011 Banff ACM Press Eprint httporoopenacuk28955
Classified as ldquoexploratory
talkrdquo
(more substantive for learning)
ldquonon-exploratoryrdquo
Given a 25 hour webinar where in the live textchat were the most effective learning conversations
De Liddo A Buckingham Shum S Quinto I Bachler M and Cannavacciuolo L Discourse-centric learning analytics 1st International Conference on Learning Analytics amp Knowledge (Banff 27 Mar-1 Apr 2011) httporoopenacuk25829
KMirsquos Cohere a web deliberation platform enabling semantic social network and discourse network analytics
Rebecca is playing the role of broker
connecting 2 peersrsquo contributions in meaningful ways
analytics for scholarly writing
42
Discourse analysis (Xerox Incremental Parser)
BACKGROUND KNOWLEDGE
Recent studies indicate hellip
hellip the previously proposed hellip
hellip is universally accepted
NOVELTY
new insights provide direct evidence
we suggest a new approach
results define a novel role
OPEN QUESTION hellip little is known hellip hellip role hellip has been elusive
Current data is insufficient hellip
GENERALIZING emerging as a promising approach Our understanding has grown exponentially growing recognition of the
importance
CONRASTING IDEAS hellip unorthodox view resolves hellip paradoxes hellip
In contrast with previous hypotheses
inconsistent with past findings
SIGNIFICANCE studies have provided important advances
Knowledge is crucial for understanding
valuable information from studies
SURPRISE We have recently observed surprisingly
We have identified unusual The recent discovery suggests intriguing roles
SUMMARIZING The goal of this study Here we show
Altogether our results indicate
Detection of salient sentences in scholarly reports based on the rhetorical signals authors use
Aacutegnes Saacutendor amp OLnet Project httpolnetorgnode512
De Liddo A Saacutendor Aacute and Buckingham Shum S Contested Collective Intelligence Rationale Technologies and a Human-Machine Annotation Study Computer Supported Cooperative Work 21 4-5 (2012) 417-448 httporoopenacuk31052
Human and machine analysis of a text for key contributions
19 sentences annotated 22 sentences annotated 11 sentences same as human annotation
71 sentences annotated 59 sentences annotated 42 sentences same as human annotation
Document 1
Document 2
httptechnologieskmiopenacukcohere20120109cohere-plus-automated-rhetorical-annotation De Liddo A Saacutendor Aacute and Buckingham Shum S Contested Collective Intelligence Rationale Technologies and a Human-Machine Annotation Study Computer Supported Cooperative Work 21 4-5 (2012) 417-448 httporoopenacuk31052
analytics for intepersonal networking
45
Semantic Social Network Analytics
De Liddo A Buckingham Shum S Quinto I Bachler M and Cannavacciuolo L Discourse-centric learning analytics 1st International Conference on Learning Analytics amp Knowledge (Banff 27 Mar-1 Apr 2011) httporoopenacuk25829
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Closing thoughts
54
55
ldquoThe basic question is not what can we measure
The basic question is
what does a good education look likerdquo
(Gardner Campbell)
httpchroniclecomblogstechtherapy20120502episode-95-learning-analytics-could-lead-to-wal-martification-of-college httplak12wikispacescomRecordings
56
Our analytics promote values pedagogy and assessment regimes
Are we clear which master
our analytics serve Are we happy to be judged by them
57
Will learning analytics merely turbocharge the current educational paradigm
mdash which is so often declared
not fit for purposehellip
58
hellipor will learning analytics reflect what we now know about designing authentic
engaged learning developing the new qualities that a
complex society demands
Defining indicators of Exploratory Talk
37
Category Indicator Challenge But if have to respond my view Critique However Irsquom not sure maybe Discussion of resources
Have you read more links
Evaluation Good example good point Explanation Means that our goals Explicit reasoning Next step relates to thatrsquos why Justification I mean we learned we observed Reflections of perspectives of others
Agree here is another makes the point take your point your view
Extract classified as Exploratory Talk
38
Time Contribution 242 PM I hate talking -P My question was whether gadgets were just
basically widgets and we could embed them in various web sites like Netvibes Google Desktop etc
242 PM Thanks thats great I am sure I understood everything but looks inspiring
243 PM Yes why OU tools not generic tools
243 PM Issues of interoperability
243 PM The new SocialLearn site looks a lot like a corkboard where you can add various widgets similar to those existing web start pages
243 PM What if we end up with as many appsgadgets as we have social networks and then we need a recommender for the apps
243 PM My question was on the definition of the crowd in the wisdom of crowds we acsess in the service model
243 PM there are various different flavours of widget eg Google gadgets W3C widgets etc SocialLearn has gone for Google gadgets
-60
-40
-20
0
20
40
60
80
92
8 9
32
93
6
94
0
94
1
94
6
95
0
95
3
95
6
10
00
10
05
10
07
10
07
10
09
10
13
10
17
10
23
10
27
10
31
10
35
10
40
10
45
10
52
10
55
11
04
11
08
11
11
11
17
11
20
11
24
11
26
11
28
11
31
11
32
11
35
11
36
11
38
11
39
11
41
11
44
11
46
11
48
11
52
11
54
12
00
12
03
12
04
12
05
Average Exploratory
Discourse analytics on webinar textchat
Sheffield UK not as sunny as yesterday - still warm Greetings from Hong Kong Morning from Wiltshire sunny here
See you bye for now bye and thank you Bye all for now
Extensions to Ferguson R and Buckingham Shum S (2011) Learning Analytics to Identify Exploratory Dialogue within Synchronous Text Chat Proc 1st Int Conf Learning Analytics amp Knowledge Feb 27-Mar 1 2011 Banff ACM Press Eprint httporoopenacuk28955
Given a 25 hour webinar where in the live textchat were the most effective learning conversations Not at the start and end of a webinar but if we zoom in on a peakhellip
Discourse analytics on webinar textchat
-100
-50
0
50
100
92
8 9
40
9
50
1
000
1
007
1
017
1
031
1
045
1
104
1
117
1
126
1
132
1
138
1
144
1
152
1
203
Averag
Wei amp He extensions to Ferguson R and Buckingham Shum S (2011) Learning Analytics to Identify Exploratory Dialogue within Synchronous Text Chat Proc 1st Int Conf Learning Analytics amp Knowledge Feb 27-Mar 1 2011 Banff ACM Press Eprint httporoopenacuk28955
Classified as ldquoexploratory
talkrdquo
(more substantive for learning)
ldquonon-exploratoryrdquo
Given a 25 hour webinar where in the live textchat were the most effective learning conversations
De Liddo A Buckingham Shum S Quinto I Bachler M and Cannavacciuolo L Discourse-centric learning analytics 1st International Conference on Learning Analytics amp Knowledge (Banff 27 Mar-1 Apr 2011) httporoopenacuk25829
KMirsquos Cohere a web deliberation platform enabling semantic social network and discourse network analytics
Rebecca is playing the role of broker
connecting 2 peersrsquo contributions in meaningful ways
analytics for scholarly writing
42
Discourse analysis (Xerox Incremental Parser)
BACKGROUND KNOWLEDGE
Recent studies indicate hellip
hellip the previously proposed hellip
hellip is universally accepted
NOVELTY
new insights provide direct evidence
we suggest a new approach
results define a novel role
OPEN QUESTION hellip little is known hellip hellip role hellip has been elusive
Current data is insufficient hellip
GENERALIZING emerging as a promising approach Our understanding has grown exponentially growing recognition of the
importance
CONRASTING IDEAS hellip unorthodox view resolves hellip paradoxes hellip
In contrast with previous hypotheses
inconsistent with past findings
SIGNIFICANCE studies have provided important advances
Knowledge is crucial for understanding
valuable information from studies
SURPRISE We have recently observed surprisingly
We have identified unusual The recent discovery suggests intriguing roles
SUMMARIZING The goal of this study Here we show
Altogether our results indicate
Detection of salient sentences in scholarly reports based on the rhetorical signals authors use
Aacutegnes Saacutendor amp OLnet Project httpolnetorgnode512
De Liddo A Saacutendor Aacute and Buckingham Shum S Contested Collective Intelligence Rationale Technologies and a Human-Machine Annotation Study Computer Supported Cooperative Work 21 4-5 (2012) 417-448 httporoopenacuk31052
Human and machine analysis of a text for key contributions
19 sentences annotated 22 sentences annotated 11 sentences same as human annotation
71 sentences annotated 59 sentences annotated 42 sentences same as human annotation
Document 1
Document 2
httptechnologieskmiopenacukcohere20120109cohere-plus-automated-rhetorical-annotation De Liddo A Saacutendor Aacute and Buckingham Shum S Contested Collective Intelligence Rationale Technologies and a Human-Machine Annotation Study Computer Supported Cooperative Work 21 4-5 (2012) 417-448 httporoopenacuk31052
analytics for intepersonal networking
45
Semantic Social Network Analytics
De Liddo A Buckingham Shum S Quinto I Bachler M and Cannavacciuolo L Discourse-centric learning analytics 1st International Conference on Learning Analytics amp Knowledge (Banff 27 Mar-1 Apr 2011) httporoopenacuk25829
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Closing thoughts
54
55
ldquoThe basic question is not what can we measure
The basic question is
what does a good education look likerdquo
(Gardner Campbell)
httpchroniclecomblogstechtherapy20120502episode-95-learning-analytics-could-lead-to-wal-martification-of-college httplak12wikispacescomRecordings
56
Our analytics promote values pedagogy and assessment regimes
Are we clear which master
our analytics serve Are we happy to be judged by them
57
Will learning analytics merely turbocharge the current educational paradigm
mdash which is so often declared
not fit for purposehellip
58
hellipor will learning analytics reflect what we now know about designing authentic
engaged learning developing the new qualities that a
complex society demands
Extract classified as Exploratory Talk
38
Time Contribution 242 PM I hate talking -P My question was whether gadgets were just
basically widgets and we could embed them in various web sites like Netvibes Google Desktop etc
242 PM Thanks thats great I am sure I understood everything but looks inspiring
243 PM Yes why OU tools not generic tools
243 PM Issues of interoperability
243 PM The new SocialLearn site looks a lot like a corkboard where you can add various widgets similar to those existing web start pages
243 PM What if we end up with as many appsgadgets as we have social networks and then we need a recommender for the apps
243 PM My question was on the definition of the crowd in the wisdom of crowds we acsess in the service model
243 PM there are various different flavours of widget eg Google gadgets W3C widgets etc SocialLearn has gone for Google gadgets
-60
-40
-20
0
20
40
60
80
92
8 9
32
93
6
94
0
94
1
94
6
95
0
95
3
95
6
10
00
10
05
10
07
10
07
10
09
10
13
10
17
10
23
10
27
10
31
10
35
10
40
10
45
10
52
10
55
11
04
11
08
11
11
11
17
11
20
11
24
11
26
11
28
11
31
11
32
11
35
11
36
11
38
11
39
11
41
11
44
11
46
11
48
11
52
11
54
12
00
12
03
12
04
12
05
Average Exploratory
Discourse analytics on webinar textchat
Sheffield UK not as sunny as yesterday - still warm Greetings from Hong Kong Morning from Wiltshire sunny here
See you bye for now bye and thank you Bye all for now
Extensions to Ferguson R and Buckingham Shum S (2011) Learning Analytics to Identify Exploratory Dialogue within Synchronous Text Chat Proc 1st Int Conf Learning Analytics amp Knowledge Feb 27-Mar 1 2011 Banff ACM Press Eprint httporoopenacuk28955
Given a 25 hour webinar where in the live textchat were the most effective learning conversations Not at the start and end of a webinar but if we zoom in on a peakhellip
Discourse analytics on webinar textchat
-100
-50
0
50
100
92
8 9
40
9
50
1
000
1
007
1
017
1
031
1
045
1
104
1
117
1
126
1
132
1
138
1
144
1
152
1
203
Averag
Wei amp He extensions to Ferguson R and Buckingham Shum S (2011) Learning Analytics to Identify Exploratory Dialogue within Synchronous Text Chat Proc 1st Int Conf Learning Analytics amp Knowledge Feb 27-Mar 1 2011 Banff ACM Press Eprint httporoopenacuk28955
Classified as ldquoexploratory
talkrdquo
(more substantive for learning)
ldquonon-exploratoryrdquo
Given a 25 hour webinar where in the live textchat were the most effective learning conversations
De Liddo A Buckingham Shum S Quinto I Bachler M and Cannavacciuolo L Discourse-centric learning analytics 1st International Conference on Learning Analytics amp Knowledge (Banff 27 Mar-1 Apr 2011) httporoopenacuk25829
KMirsquos Cohere a web deliberation platform enabling semantic social network and discourse network analytics
Rebecca is playing the role of broker
connecting 2 peersrsquo contributions in meaningful ways
analytics for scholarly writing
42
Discourse analysis (Xerox Incremental Parser)
BACKGROUND KNOWLEDGE
Recent studies indicate hellip
hellip the previously proposed hellip
hellip is universally accepted
NOVELTY
new insights provide direct evidence
we suggest a new approach
results define a novel role
OPEN QUESTION hellip little is known hellip hellip role hellip has been elusive
Current data is insufficient hellip
GENERALIZING emerging as a promising approach Our understanding has grown exponentially growing recognition of the
importance
CONRASTING IDEAS hellip unorthodox view resolves hellip paradoxes hellip
In contrast with previous hypotheses
inconsistent with past findings
SIGNIFICANCE studies have provided important advances
Knowledge is crucial for understanding
valuable information from studies
SURPRISE We have recently observed surprisingly
We have identified unusual The recent discovery suggests intriguing roles
SUMMARIZING The goal of this study Here we show
Altogether our results indicate
Detection of salient sentences in scholarly reports based on the rhetorical signals authors use
Aacutegnes Saacutendor amp OLnet Project httpolnetorgnode512
De Liddo A Saacutendor Aacute and Buckingham Shum S Contested Collective Intelligence Rationale Technologies and a Human-Machine Annotation Study Computer Supported Cooperative Work 21 4-5 (2012) 417-448 httporoopenacuk31052
Human and machine analysis of a text for key contributions
19 sentences annotated 22 sentences annotated 11 sentences same as human annotation
71 sentences annotated 59 sentences annotated 42 sentences same as human annotation
Document 1
Document 2
httptechnologieskmiopenacukcohere20120109cohere-plus-automated-rhetorical-annotation De Liddo A Saacutendor Aacute and Buckingham Shum S Contested Collective Intelligence Rationale Technologies and a Human-Machine Annotation Study Computer Supported Cooperative Work 21 4-5 (2012) 417-448 httporoopenacuk31052
analytics for intepersonal networking
45
Semantic Social Network Analytics
De Liddo A Buckingham Shum S Quinto I Bachler M and Cannavacciuolo L Discourse-centric learning analytics 1st International Conference on Learning Analytics amp Knowledge (Banff 27 Mar-1 Apr 2011) httporoopenacuk25829
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Closing thoughts
54
55
ldquoThe basic question is not what can we measure
The basic question is
what does a good education look likerdquo
(Gardner Campbell)
httpchroniclecomblogstechtherapy20120502episode-95-learning-analytics-could-lead-to-wal-martification-of-college httplak12wikispacescomRecordings
56
Our analytics promote values pedagogy and assessment regimes
Are we clear which master
our analytics serve Are we happy to be judged by them
57
Will learning analytics merely turbocharge the current educational paradigm
mdash which is so often declared
not fit for purposehellip
58
hellipor will learning analytics reflect what we now know about designing authentic
engaged learning developing the new qualities that a
complex society demands
-60
-40
-20
0
20
40
60
80
92
8 9
32
93
6
94
0
94
1
94
6
95
0
95
3
95
6
10
00
10
05
10
07
10
07
10
09
10
13
10
17
10
23
10
27
10
31
10
35
10
40
10
45
10
52
10
55
11
04
11
08
11
11
11
17
11
20
11
24
11
26
11
28
11
31
11
32
11
35
11
36
11
38
11
39
11
41
11
44
11
46
11
48
11
52
11
54
12
00
12
03
12
04
12
05
Average Exploratory
Discourse analytics on webinar textchat
Sheffield UK not as sunny as yesterday - still warm Greetings from Hong Kong Morning from Wiltshire sunny here
See you bye for now bye and thank you Bye all for now
Extensions to Ferguson R and Buckingham Shum S (2011) Learning Analytics to Identify Exploratory Dialogue within Synchronous Text Chat Proc 1st Int Conf Learning Analytics amp Knowledge Feb 27-Mar 1 2011 Banff ACM Press Eprint httporoopenacuk28955
Given a 25 hour webinar where in the live textchat were the most effective learning conversations Not at the start and end of a webinar but if we zoom in on a peakhellip
Discourse analytics on webinar textchat
-100
-50
0
50
100
92
8 9
40
9
50
1
000
1
007
1
017
1
031
1
045
1
104
1
117
1
126
1
132
1
138
1
144
1
152
1
203
Averag
Wei amp He extensions to Ferguson R and Buckingham Shum S (2011) Learning Analytics to Identify Exploratory Dialogue within Synchronous Text Chat Proc 1st Int Conf Learning Analytics amp Knowledge Feb 27-Mar 1 2011 Banff ACM Press Eprint httporoopenacuk28955
Classified as ldquoexploratory
talkrdquo
(more substantive for learning)
ldquonon-exploratoryrdquo
Given a 25 hour webinar where in the live textchat were the most effective learning conversations
De Liddo A Buckingham Shum S Quinto I Bachler M and Cannavacciuolo L Discourse-centric learning analytics 1st International Conference on Learning Analytics amp Knowledge (Banff 27 Mar-1 Apr 2011) httporoopenacuk25829
KMirsquos Cohere a web deliberation platform enabling semantic social network and discourse network analytics
Rebecca is playing the role of broker
connecting 2 peersrsquo contributions in meaningful ways
analytics for scholarly writing
42
Discourse analysis (Xerox Incremental Parser)
BACKGROUND KNOWLEDGE
Recent studies indicate hellip
hellip the previously proposed hellip
hellip is universally accepted
NOVELTY
new insights provide direct evidence
we suggest a new approach
results define a novel role
OPEN QUESTION hellip little is known hellip hellip role hellip has been elusive
Current data is insufficient hellip
GENERALIZING emerging as a promising approach Our understanding has grown exponentially growing recognition of the
importance
CONRASTING IDEAS hellip unorthodox view resolves hellip paradoxes hellip
In contrast with previous hypotheses
inconsistent with past findings
SIGNIFICANCE studies have provided important advances
Knowledge is crucial for understanding
valuable information from studies
SURPRISE We have recently observed surprisingly
We have identified unusual The recent discovery suggests intriguing roles
SUMMARIZING The goal of this study Here we show
Altogether our results indicate
Detection of salient sentences in scholarly reports based on the rhetorical signals authors use
Aacutegnes Saacutendor amp OLnet Project httpolnetorgnode512
De Liddo A Saacutendor Aacute and Buckingham Shum S Contested Collective Intelligence Rationale Technologies and a Human-Machine Annotation Study Computer Supported Cooperative Work 21 4-5 (2012) 417-448 httporoopenacuk31052
Human and machine analysis of a text for key contributions
19 sentences annotated 22 sentences annotated 11 sentences same as human annotation
71 sentences annotated 59 sentences annotated 42 sentences same as human annotation
Document 1
Document 2
httptechnologieskmiopenacukcohere20120109cohere-plus-automated-rhetorical-annotation De Liddo A Saacutendor Aacute and Buckingham Shum S Contested Collective Intelligence Rationale Technologies and a Human-Machine Annotation Study Computer Supported Cooperative Work 21 4-5 (2012) 417-448 httporoopenacuk31052
analytics for intepersonal networking
45
Semantic Social Network Analytics
De Liddo A Buckingham Shum S Quinto I Bachler M and Cannavacciuolo L Discourse-centric learning analytics 1st International Conference on Learning Analytics amp Knowledge (Banff 27 Mar-1 Apr 2011) httporoopenacuk25829
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Closing thoughts
54
55
ldquoThe basic question is not what can we measure
The basic question is
what does a good education look likerdquo
(Gardner Campbell)
httpchroniclecomblogstechtherapy20120502episode-95-learning-analytics-could-lead-to-wal-martification-of-college httplak12wikispacescomRecordings
56
Our analytics promote values pedagogy and assessment regimes
Are we clear which master
our analytics serve Are we happy to be judged by them
57
Will learning analytics merely turbocharge the current educational paradigm
mdash which is so often declared
not fit for purposehellip
58
hellipor will learning analytics reflect what we now know about designing authentic
engaged learning developing the new qualities that a
complex society demands
Discourse analytics on webinar textchat
-100
-50
0
50
100
92
8 9
40
9
50
1
000
1
007
1
017
1
031
1
045
1
104
1
117
1
126
1
132
1
138
1
144
1
152
1
203
Averag
Wei amp He extensions to Ferguson R and Buckingham Shum S (2011) Learning Analytics to Identify Exploratory Dialogue within Synchronous Text Chat Proc 1st Int Conf Learning Analytics amp Knowledge Feb 27-Mar 1 2011 Banff ACM Press Eprint httporoopenacuk28955
Classified as ldquoexploratory
talkrdquo
(more substantive for learning)
ldquonon-exploratoryrdquo
Given a 25 hour webinar where in the live textchat were the most effective learning conversations
De Liddo A Buckingham Shum S Quinto I Bachler M and Cannavacciuolo L Discourse-centric learning analytics 1st International Conference on Learning Analytics amp Knowledge (Banff 27 Mar-1 Apr 2011) httporoopenacuk25829
KMirsquos Cohere a web deliberation platform enabling semantic social network and discourse network analytics
Rebecca is playing the role of broker
connecting 2 peersrsquo contributions in meaningful ways
analytics for scholarly writing
42
Discourse analysis (Xerox Incremental Parser)
BACKGROUND KNOWLEDGE
Recent studies indicate hellip
hellip the previously proposed hellip
hellip is universally accepted
NOVELTY
new insights provide direct evidence
we suggest a new approach
results define a novel role
OPEN QUESTION hellip little is known hellip hellip role hellip has been elusive
Current data is insufficient hellip
GENERALIZING emerging as a promising approach Our understanding has grown exponentially growing recognition of the
importance
CONRASTING IDEAS hellip unorthodox view resolves hellip paradoxes hellip
In contrast with previous hypotheses
inconsistent with past findings
SIGNIFICANCE studies have provided important advances
Knowledge is crucial for understanding
valuable information from studies
SURPRISE We have recently observed surprisingly
We have identified unusual The recent discovery suggests intriguing roles
SUMMARIZING The goal of this study Here we show
Altogether our results indicate
Detection of salient sentences in scholarly reports based on the rhetorical signals authors use
Aacutegnes Saacutendor amp OLnet Project httpolnetorgnode512
De Liddo A Saacutendor Aacute and Buckingham Shum S Contested Collective Intelligence Rationale Technologies and a Human-Machine Annotation Study Computer Supported Cooperative Work 21 4-5 (2012) 417-448 httporoopenacuk31052
Human and machine analysis of a text for key contributions
19 sentences annotated 22 sentences annotated 11 sentences same as human annotation
71 sentences annotated 59 sentences annotated 42 sentences same as human annotation
Document 1
Document 2
httptechnologieskmiopenacukcohere20120109cohere-plus-automated-rhetorical-annotation De Liddo A Saacutendor Aacute and Buckingham Shum S Contested Collective Intelligence Rationale Technologies and a Human-Machine Annotation Study Computer Supported Cooperative Work 21 4-5 (2012) 417-448 httporoopenacuk31052
analytics for intepersonal networking
45
Semantic Social Network Analytics
De Liddo A Buckingham Shum S Quinto I Bachler M and Cannavacciuolo L Discourse-centric learning analytics 1st International Conference on Learning Analytics amp Knowledge (Banff 27 Mar-1 Apr 2011) httporoopenacuk25829
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Closing thoughts
54
55
ldquoThe basic question is not what can we measure
The basic question is
what does a good education look likerdquo
(Gardner Campbell)
httpchroniclecomblogstechtherapy20120502episode-95-learning-analytics-could-lead-to-wal-martification-of-college httplak12wikispacescomRecordings
56
Our analytics promote values pedagogy and assessment regimes
Are we clear which master
our analytics serve Are we happy to be judged by them
57
Will learning analytics merely turbocharge the current educational paradigm
mdash which is so often declared
not fit for purposehellip
58
hellipor will learning analytics reflect what we now know about designing authentic
engaged learning developing the new qualities that a
complex society demands
De Liddo A Buckingham Shum S Quinto I Bachler M and Cannavacciuolo L Discourse-centric learning analytics 1st International Conference on Learning Analytics amp Knowledge (Banff 27 Mar-1 Apr 2011) httporoopenacuk25829
KMirsquos Cohere a web deliberation platform enabling semantic social network and discourse network analytics
Rebecca is playing the role of broker
connecting 2 peersrsquo contributions in meaningful ways
analytics for scholarly writing
42
Discourse analysis (Xerox Incremental Parser)
BACKGROUND KNOWLEDGE
Recent studies indicate hellip
hellip the previously proposed hellip
hellip is universally accepted
NOVELTY
new insights provide direct evidence
we suggest a new approach
results define a novel role
OPEN QUESTION hellip little is known hellip hellip role hellip has been elusive
Current data is insufficient hellip
GENERALIZING emerging as a promising approach Our understanding has grown exponentially growing recognition of the
importance
CONRASTING IDEAS hellip unorthodox view resolves hellip paradoxes hellip
In contrast with previous hypotheses
inconsistent with past findings
SIGNIFICANCE studies have provided important advances
Knowledge is crucial for understanding
valuable information from studies
SURPRISE We have recently observed surprisingly
We have identified unusual The recent discovery suggests intriguing roles
SUMMARIZING The goal of this study Here we show
Altogether our results indicate
Detection of salient sentences in scholarly reports based on the rhetorical signals authors use
Aacutegnes Saacutendor amp OLnet Project httpolnetorgnode512
De Liddo A Saacutendor Aacute and Buckingham Shum S Contested Collective Intelligence Rationale Technologies and a Human-Machine Annotation Study Computer Supported Cooperative Work 21 4-5 (2012) 417-448 httporoopenacuk31052
Human and machine analysis of a text for key contributions
19 sentences annotated 22 sentences annotated 11 sentences same as human annotation
71 sentences annotated 59 sentences annotated 42 sentences same as human annotation
Document 1
Document 2
httptechnologieskmiopenacukcohere20120109cohere-plus-automated-rhetorical-annotation De Liddo A Saacutendor Aacute and Buckingham Shum S Contested Collective Intelligence Rationale Technologies and a Human-Machine Annotation Study Computer Supported Cooperative Work 21 4-5 (2012) 417-448 httporoopenacuk31052
analytics for intepersonal networking
45
Semantic Social Network Analytics
De Liddo A Buckingham Shum S Quinto I Bachler M and Cannavacciuolo L Discourse-centric learning analytics 1st International Conference on Learning Analytics amp Knowledge (Banff 27 Mar-1 Apr 2011) httporoopenacuk25829
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Closing thoughts
54
55
ldquoThe basic question is not what can we measure
The basic question is
what does a good education look likerdquo
(Gardner Campbell)
httpchroniclecomblogstechtherapy20120502episode-95-learning-analytics-could-lead-to-wal-martification-of-college httplak12wikispacescomRecordings
56
Our analytics promote values pedagogy and assessment regimes
Are we clear which master
our analytics serve Are we happy to be judged by them
57
Will learning analytics merely turbocharge the current educational paradigm
mdash which is so often declared
not fit for purposehellip
58
hellipor will learning analytics reflect what we now know about designing authentic
engaged learning developing the new qualities that a
complex society demands
analytics for scholarly writing
42
Discourse analysis (Xerox Incremental Parser)
BACKGROUND KNOWLEDGE
Recent studies indicate hellip
hellip the previously proposed hellip
hellip is universally accepted
NOVELTY
new insights provide direct evidence
we suggest a new approach
results define a novel role
OPEN QUESTION hellip little is known hellip hellip role hellip has been elusive
Current data is insufficient hellip
GENERALIZING emerging as a promising approach Our understanding has grown exponentially growing recognition of the
importance
CONRASTING IDEAS hellip unorthodox view resolves hellip paradoxes hellip
In contrast with previous hypotheses
inconsistent with past findings
SIGNIFICANCE studies have provided important advances
Knowledge is crucial for understanding
valuable information from studies
SURPRISE We have recently observed surprisingly
We have identified unusual The recent discovery suggests intriguing roles
SUMMARIZING The goal of this study Here we show
Altogether our results indicate
Detection of salient sentences in scholarly reports based on the rhetorical signals authors use
Aacutegnes Saacutendor amp OLnet Project httpolnetorgnode512
De Liddo A Saacutendor Aacute and Buckingham Shum S Contested Collective Intelligence Rationale Technologies and a Human-Machine Annotation Study Computer Supported Cooperative Work 21 4-5 (2012) 417-448 httporoopenacuk31052
Human and machine analysis of a text for key contributions
19 sentences annotated 22 sentences annotated 11 sentences same as human annotation
71 sentences annotated 59 sentences annotated 42 sentences same as human annotation
Document 1
Document 2
httptechnologieskmiopenacukcohere20120109cohere-plus-automated-rhetorical-annotation De Liddo A Saacutendor Aacute and Buckingham Shum S Contested Collective Intelligence Rationale Technologies and a Human-Machine Annotation Study Computer Supported Cooperative Work 21 4-5 (2012) 417-448 httporoopenacuk31052
analytics for intepersonal networking
45
Semantic Social Network Analytics
De Liddo A Buckingham Shum S Quinto I Bachler M and Cannavacciuolo L Discourse-centric learning analytics 1st International Conference on Learning Analytics amp Knowledge (Banff 27 Mar-1 Apr 2011) httporoopenacuk25829
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Closing thoughts
54
55
ldquoThe basic question is not what can we measure
The basic question is
what does a good education look likerdquo
(Gardner Campbell)
httpchroniclecomblogstechtherapy20120502episode-95-learning-analytics-could-lead-to-wal-martification-of-college httplak12wikispacescomRecordings
56
Our analytics promote values pedagogy and assessment regimes
Are we clear which master
our analytics serve Are we happy to be judged by them
57
Will learning analytics merely turbocharge the current educational paradigm
mdash which is so often declared
not fit for purposehellip
58
hellipor will learning analytics reflect what we now know about designing authentic
engaged learning developing the new qualities that a
complex society demands
Discourse analysis (Xerox Incremental Parser)
BACKGROUND KNOWLEDGE
Recent studies indicate hellip
hellip the previously proposed hellip
hellip is universally accepted
NOVELTY
new insights provide direct evidence
we suggest a new approach
results define a novel role
OPEN QUESTION hellip little is known hellip hellip role hellip has been elusive
Current data is insufficient hellip
GENERALIZING emerging as a promising approach Our understanding has grown exponentially growing recognition of the
importance
CONRASTING IDEAS hellip unorthodox view resolves hellip paradoxes hellip
In contrast with previous hypotheses
inconsistent with past findings
SIGNIFICANCE studies have provided important advances
Knowledge is crucial for understanding
valuable information from studies
SURPRISE We have recently observed surprisingly
We have identified unusual The recent discovery suggests intriguing roles
SUMMARIZING The goal of this study Here we show
Altogether our results indicate
Detection of salient sentences in scholarly reports based on the rhetorical signals authors use
Aacutegnes Saacutendor amp OLnet Project httpolnetorgnode512
De Liddo A Saacutendor Aacute and Buckingham Shum S Contested Collective Intelligence Rationale Technologies and a Human-Machine Annotation Study Computer Supported Cooperative Work 21 4-5 (2012) 417-448 httporoopenacuk31052
Human and machine analysis of a text for key contributions
19 sentences annotated 22 sentences annotated 11 sentences same as human annotation
71 sentences annotated 59 sentences annotated 42 sentences same as human annotation
Document 1
Document 2
httptechnologieskmiopenacukcohere20120109cohere-plus-automated-rhetorical-annotation De Liddo A Saacutendor Aacute and Buckingham Shum S Contested Collective Intelligence Rationale Technologies and a Human-Machine Annotation Study Computer Supported Cooperative Work 21 4-5 (2012) 417-448 httporoopenacuk31052
analytics for intepersonal networking
45
Semantic Social Network Analytics
De Liddo A Buckingham Shum S Quinto I Bachler M and Cannavacciuolo L Discourse-centric learning analytics 1st International Conference on Learning Analytics amp Knowledge (Banff 27 Mar-1 Apr 2011) httporoopenacuk25829
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Closing thoughts
54
55
ldquoThe basic question is not what can we measure
The basic question is
what does a good education look likerdquo
(Gardner Campbell)
httpchroniclecomblogstechtherapy20120502episode-95-learning-analytics-could-lead-to-wal-martification-of-college httplak12wikispacescomRecordings
56
Our analytics promote values pedagogy and assessment regimes
Are we clear which master
our analytics serve Are we happy to be judged by them
57
Will learning analytics merely turbocharge the current educational paradigm
mdash which is so often declared
not fit for purposehellip
58
hellipor will learning analytics reflect what we now know about designing authentic
engaged learning developing the new qualities that a
complex society demands
Human and machine analysis of a text for key contributions
19 sentences annotated 22 sentences annotated 11 sentences same as human annotation
71 sentences annotated 59 sentences annotated 42 sentences same as human annotation
Document 1
Document 2
httptechnologieskmiopenacukcohere20120109cohere-plus-automated-rhetorical-annotation De Liddo A Saacutendor Aacute and Buckingham Shum S Contested Collective Intelligence Rationale Technologies and a Human-Machine Annotation Study Computer Supported Cooperative Work 21 4-5 (2012) 417-448 httporoopenacuk31052
analytics for intepersonal networking
45
Semantic Social Network Analytics
De Liddo A Buckingham Shum S Quinto I Bachler M and Cannavacciuolo L Discourse-centric learning analytics 1st International Conference on Learning Analytics amp Knowledge (Banff 27 Mar-1 Apr 2011) httporoopenacuk25829
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Closing thoughts
54
55
ldquoThe basic question is not what can we measure
The basic question is
what does a good education look likerdquo
(Gardner Campbell)
httpchroniclecomblogstechtherapy20120502episode-95-learning-analytics-could-lead-to-wal-martification-of-college httplak12wikispacescomRecordings
56
Our analytics promote values pedagogy and assessment regimes
Are we clear which master
our analytics serve Are we happy to be judged by them
57
Will learning analytics merely turbocharge the current educational paradigm
mdash which is so often declared
not fit for purposehellip
58
hellipor will learning analytics reflect what we now know about designing authentic
engaged learning developing the new qualities that a
complex society demands
analytics for intepersonal networking
45
Semantic Social Network Analytics
De Liddo A Buckingham Shum S Quinto I Bachler M and Cannavacciuolo L Discourse-centric learning analytics 1st International Conference on Learning Analytics amp Knowledge (Banff 27 Mar-1 Apr 2011) httporoopenacuk25829
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Closing thoughts
54
55
ldquoThe basic question is not what can we measure
The basic question is
what does a good education look likerdquo
(Gardner Campbell)
httpchroniclecomblogstechtherapy20120502episode-95-learning-analytics-could-lead-to-wal-martification-of-college httplak12wikispacescomRecordings
56
Our analytics promote values pedagogy and assessment regimes
Are we clear which master
our analytics serve Are we happy to be judged by them
57
Will learning analytics merely turbocharge the current educational paradigm
mdash which is so often declared
not fit for purposehellip
58
hellipor will learning analytics reflect what we now know about designing authentic
engaged learning developing the new qualities that a
complex society demands
Semantic Social Network Analytics
De Liddo A Buckingham Shum S Quinto I Bachler M and Cannavacciuolo L Discourse-centric learning analytics 1st International Conference on Learning Analytics amp Knowledge (Banff 27 Mar-1 Apr 2011) httporoopenacuk25829
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Closing thoughts
54
55
ldquoThe basic question is not what can we measure
The basic question is
what does a good education look likerdquo
(Gardner Campbell)
httpchroniclecomblogstechtherapy20120502episode-95-learning-analytics-could-lead-to-wal-martification-of-college httplak12wikispacescomRecordings
56
Our analytics promote values pedagogy and assessment regimes
Are we clear which master
our analytics serve Are we happy to be judged by them
57
Will learning analytics merely turbocharge the current educational paradigm
mdash which is so often declared
not fit for purposehellip
58
hellipor will learning analytics reflect what we now know about designing authentic
engaged learning developing the new qualities that a
complex society demands
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Closing thoughts
54
55
ldquoThe basic question is not what can we measure
The basic question is
what does a good education look likerdquo
(Gardner Campbell)
httpchroniclecomblogstechtherapy20120502episode-95-learning-analytics-could-lead-to-wal-martification-of-college httplak12wikispacescomRecordings
56
Our analytics promote values pedagogy and assessment regimes
Are we clear which master
our analytics serve Are we happy to be judged by them
57
Will learning analytics merely turbocharge the current educational paradigm
mdash which is so often declared
not fit for purposehellip
58
hellipor will learning analytics reflect what we now know about designing authentic
engaged learning developing the new qualities that a
complex society demands
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Closing thoughts
54
55
ldquoThe basic question is not what can we measure
The basic question is
what does a good education look likerdquo
(Gardner Campbell)
httpchroniclecomblogstechtherapy20120502episode-95-learning-analytics-could-lead-to-wal-martification-of-college httplak12wikispacescomRecordings
56
Our analytics promote values pedagogy and assessment regimes
Are we clear which master
our analytics serve Are we happy to be judged by them
57
Will learning analytics merely turbocharge the current educational paradigm
mdash which is so often declared
not fit for purposehellip
58
hellipor will learning analytics reflect what we now know about designing authentic
engaged learning developing the new qualities that a
complex society demands
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Closing thoughts
54
55
ldquoThe basic question is not what can we measure
The basic question is
what does a good education look likerdquo
(Gardner Campbell)
httpchroniclecomblogstechtherapy20120502episode-95-learning-analytics-could-lead-to-wal-martification-of-college httplak12wikispacescomRecordings
56
Our analytics promote values pedagogy and assessment regimes
Are we clear which master
our analytics serve Are we happy to be judged by them
57
Will learning analytics merely turbocharge the current educational paradigm
mdash which is so often declared
not fit for purposehellip
58
hellipor will learning analytics reflect what we now know about designing authentic
engaged learning developing the new qualities that a
complex society demands
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Closing thoughts
54
55
ldquoThe basic question is not what can we measure
The basic question is
what does a good education look likerdquo
(Gardner Campbell)
httpchroniclecomblogstechtherapy20120502episode-95-learning-analytics-could-lead-to-wal-martification-of-college httplak12wikispacescomRecordings
56
Our analytics promote values pedagogy and assessment regimes
Are we clear which master
our analytics serve Are we happy to be judged by them
57
Will learning analytics merely turbocharge the current educational paradigm
mdash which is so often declared
not fit for purposehellip
58
hellipor will learning analytics reflect what we now know about designing authentic
engaged learning developing the new qualities that a
complex society demands
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Closing thoughts
54
55
ldquoThe basic question is not what can we measure
The basic question is
what does a good education look likerdquo
(Gardner Campbell)
httpchroniclecomblogstechtherapy20120502episode-95-learning-analytics-could-lead-to-wal-martification-of-college httplak12wikispacescomRecordings
56
Our analytics promote values pedagogy and assessment regimes
Are we clear which master
our analytics serve Are we happy to be judged by them
57
Will learning analytics merely turbocharge the current educational paradigm
mdash which is so often declared
not fit for purposehellip
58
hellipor will learning analytics reflect what we now know about designing authentic
engaged learning developing the new qualities that a
complex society demands
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Closing thoughts
54
55
ldquoThe basic question is not what can we measure
The basic question is
what does a good education look likerdquo
(Gardner Campbell)
httpchroniclecomblogstechtherapy20120502episode-95-learning-analytics-could-lead-to-wal-martification-of-college httplak12wikispacescomRecordings
56
Our analytics promote values pedagogy and assessment regimes
Are we clear which master
our analytics serve Are we happy to be judged by them
57
Will learning analytics merely turbocharge the current educational paradigm
mdash which is so often declared
not fit for purposehellip
58
hellipor will learning analytics reflect what we now know about designing authentic
engaged learning developing the new qualities that a
complex society demands
Visualizing and filtering social ties in SocialLearn by topic and type
Visualising Social Learning in the SocialLearn Environment Bieke Schreurs and Maarten de Laat (Open University The Netherlands) Chris Teplovs (Problemshift Inc and University of Windsor) Rebecca Ferguson and Simon Buckingham Shum (Open University UK) SoLAR Storm webinar Open University UK httpbitlyUaFhbL
Closing thoughts
54
55
ldquoThe basic question is not what can we measure
The basic question is
what does a good education look likerdquo
(Gardner Campbell)
httpchroniclecomblogstechtherapy20120502episode-95-learning-analytics-could-lead-to-wal-martification-of-college httplak12wikispacescomRecordings
56
Our analytics promote values pedagogy and assessment regimes
Are we clear which master
our analytics serve Are we happy to be judged by them
57
Will learning analytics merely turbocharge the current educational paradigm
mdash which is so often declared
not fit for purposehellip
58
hellipor will learning analytics reflect what we now know about designing authentic
engaged learning developing the new qualities that a
complex society demands
Closing thoughts
54
55
ldquoThe basic question is not what can we measure
The basic question is
what does a good education look likerdquo
(Gardner Campbell)
httpchroniclecomblogstechtherapy20120502episode-95-learning-analytics-could-lead-to-wal-martification-of-college httplak12wikispacescomRecordings
56
Our analytics promote values pedagogy and assessment regimes
Are we clear which master
our analytics serve Are we happy to be judged by them
57
Will learning analytics merely turbocharge the current educational paradigm
mdash which is so often declared
not fit for purposehellip
58
hellipor will learning analytics reflect what we now know about designing authentic
engaged learning developing the new qualities that a
complex society demands
55
ldquoThe basic question is not what can we measure
The basic question is
what does a good education look likerdquo
(Gardner Campbell)
httpchroniclecomblogstechtherapy20120502episode-95-learning-analytics-could-lead-to-wal-martification-of-college httplak12wikispacescomRecordings
56
Our analytics promote values pedagogy and assessment regimes
Are we clear which master
our analytics serve Are we happy to be judged by them
57
Will learning analytics merely turbocharge the current educational paradigm
mdash which is so often declared
not fit for purposehellip
58
hellipor will learning analytics reflect what we now know about designing authentic
engaged learning developing the new qualities that a
complex society demands
56
Our analytics promote values pedagogy and assessment regimes
Are we clear which master
our analytics serve Are we happy to be judged by them
57
Will learning analytics merely turbocharge the current educational paradigm
mdash which is so often declared
not fit for purposehellip
58
hellipor will learning analytics reflect what we now know about designing authentic
engaged learning developing the new qualities that a
complex society demands
57
Will learning analytics merely turbocharge the current educational paradigm
mdash which is so often declared
not fit for purposehellip
58
hellipor will learning analytics reflect what we now know about designing authentic
engaged learning developing the new qualities that a
complex society demands
58
hellipor will learning analytics reflect what we now know about designing authentic
engaged learning developing the new qualities that a
complex society demands