our learning analytics are our pedagogy
DESCRIPTION
Keynote Address, Expanding Horizons 2012, Macquarie University http://staff.mq.edu.au/teaching/workshops_programs/expanding_horizons "Learning Analytics": unprecedented data sets and live data streams about learners, with computational power to help make sense of it all, and new breeds of staff who can talk predictive models, pedagogy and ethics. This means rather different things to different people: unprecedented opportunity to study, benchmark and improve educational practice, at scales from countries and institutions, to departments, individual teachers and learners. "Benchmarking" may trigger dystopic visions of dumbed down proxies for 'real teaching and learning', but an emu response is no good. For educational institutions, our calling is to raise the quality of debate, shape external and internal policy, and engage with the companies and open communities developing the future infrastructure. How we deploy these new tools rests critically on assessment regimes, what can be logged and measured with integrity, and what we think it means to deliver education that equips citizens for a complex, uncertain world.TRANSCRIPT
Our Learning Analytics are Our Pedagogy
Simon Buckingham Shum Knowledge Media Institute, The Open University UK simon.buckinghamshum.net
Keynote Address, Expanding Horizons 2012, Macquarie University http://staff.mq.edu.au/teaching/workshops_programs/expanding_horizons
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@sbskmi
learning objective:
walk out with
better questions than you can ask right now
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Urgent need: quality dialogue between analytics stakeholders, to accelerate invention innovation
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www.SoLAResearch.org Follow: @SoLAResearch
The global demand for learning
4 http://www.col.org/resources/speeches/2012presentations/Pages/2012-02-01.aspx
Implications for assessment and
feedback at massive scale?
John Daniel
is education poised to become a data-driven enterprise and science
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?
Possibly 90% of the digital data we have today was generated in the last 2 years
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Volume outstrips old infrastructure
Variety Internet of things, e-business transactions, environmental sensors, social media, audio, video, mobile…
Velocity The speed of data access and analysis is exploding
A quantitative shift on this scale is in fact a qualitative shift, requiring
new ways of thinking about societal phenomena
edX: “this is big data, giving us the chance to ask big questions about learning”
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Will the tomorrow’s educational researcher be
as helpless without an analytics infrastructure, as
a geneticist without genome databases, or a physicist without CERN?
Lifelogging: explosion of data capture and sharing about personal activities
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http://www.mirror-project.eu
http://quantifiedself.com/guide
Educational Data Mining research community
Learning Analytics research community
different levels of analytic
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‘Learning Analytics’ and ‘Academic Analytics’
Long, P. and Siemens, G. (2011), Penetrating the fog: analytics in learning and education. Educause Review Online, 46, 5, pp.31-40. http://www.educause.edu/ero/article/penetrating-fog-analytics-learning-and-education 12
Macro/Meso/Micro Learning Analytics
Macro: region/state/national/international
Macro/Meso/Micro Learning Analytics
Meso: institution-wide
Macro: region/state/national/international
Micro: individual user actions
(and hence cohort)
Macro/Meso/Micro Learning Analytics
Meso: institution-wide
Macro: region/state/national/international
Will institutions be dazzled by the dashboards, or know what
questions to ask at each level?
Macro/Meso/Micro Learning Analytics
Macro: region/state/national/international
US states are getting the infrastructure in place
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dataqualitycampaign.org
National league tables for English schools
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Macro/Meso/Micro Learning Analytics
Meso: institution-wide
Analytics-savvy Leaders are the future?
21 Parr-Rud, O. (2012). Drive Your Business with Predictive Analytics. SAS White Paper http://www.sas.com/reg/gen/corp/1800392
Business Intelligence companies see an education market opening up
22 http://www.sas.com/industry/education/highered
These are pedagogically agnostic: they seek to optimize operational
efficiency whatever the sector
These may make pedagogical assumptions: how will learning
design and assessment regimes shape the analytics they offer?
Business Intelligence companies see an education market opening up
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…but do they know anything about the roles that language plays in
learning and knowledge construction?
BI+HigherEd communities of practice
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Business Intelligence
≠ Learning Analytics
Micro: individual user actions
(and hence cohort)
Macro/Meso/Micro Learning Analytics
Analytics in your VLE: Blackboard: feedback to students
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http://www.blackboard.com/Platforms/Analytics/Overview.aspx
Purdue University Signals: real time traffic-lights for students based on predictive model
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Premise: academic success is defined as a function of aptitude (as measured by standardized test scores and similar information) and effort (as measured by participation within the online learning environment). Using factor analysis and logistic regression, a model was tested to predict student success based on:
• ACT or SAT score • Overall grade-point average • CMS usage composite • CMS assessment composite • CMS assignment composite • CMS calendar composite
Campbell et al (2007). Academic Analytics: A New Tool for a New Era, EDUCAUSE Review, vol. 42, no. 4 (July/August 2007): 40–57. http://bit.ly/lmxG2x
Predicted 66%-80% of struggling students who needed help
Desire2Learn visual analytics & predictive models which can be interrogated on different dimensions
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http://www.desire2learn.com/products/analytics
Desire2Learn visual analytics & predictive models which can be interrogated on different dimensions
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http://www.desire2learn.com/products/analytics
Khan Academy: more data to teachers, finer-grained feedback to students
32 http://www.thegatesnotes.com/Topics/Education/Sal-Khan-Analytics-Khan-Academy
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https://grockit.com/research
Adaptive platforms generate fine-grained analytics
The VLE—BI convergence
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Micro: individual user actions
(and hence cohort)
Hard distinctions between Learning + Academic analytics may dissolve
Meso: institution-wide
Macro: region/state/national/international
Aggregation of user traces enriches meso + macro analytics with finer-grained process data
…as they get joined up, each level enriches the others
Micro: individual user actions
(and hence cohort)
Hard distinctions between Learning + Academic analytics may dissolve
Meso: institution-wide
Macro: region/state/national/international
Aggregation of user traces enriches meso + macro analytics with finer-grained process data
Breadth + depth from macro + meso levels add power to
micro analytics
…as they get joined up, each level enriches the others
…so everybody’s happy?
dawn of a new data-driven enterprise + science?
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wrong.
a very healthy debate is brewing…
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data (indeed technology)
is not neutral
data does not wholly ‘speak for itself’
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Measurement tools are not neutral
“accounting tools...do not simply aid the measurement of economic activity, they shape the reality they measure”
Du Gay, P. and Pryke, M. (2002) Cultural Economy: Cultural Analysis and Commercial Life
Sage, London. pp. 12-13
Beyond big data hubris
1. Automating Research Changes the Definition of Knowledge
2. Claims to Objectivity and Accuracy are Misleading 3. Bigger Data are Not Always Better Data 4. Not All Data Are Equivalent 5. Just Because it is Accessible Doesn’t Make it
Ethical 6. Limited Access to Big Data Creates New Digital
Divides
boyd, d. and Crawford, K. (2001). Six Provocations for Big Data. Presented to: A Decade in Internet Time: Symposium on the
Dynamics of the Internet and Society, Oxford Internet Institute, Sept. 21, 2011.
http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1926431
Analytics provide maps = systematic ways of distorting reality
“A marker of the health of the learning analytics field will be the quality of debate around what the technology renders visible and leaves invisible.”
Buckingham Shum, S. and Deakin Crick, R. (2012). Learning Dispositions and Transferable Competencies: Pedagogy, Modelling
and Learning Analytics. Proc. 2nd Int. Conf. Learning Analytics & Knowledge. (29 Apr-2 May, 2012, Vancouver, BC). ACM: New York.
Eprint: http://oro.open.ac.uk/32823
context
context
context
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Analytics in English schools RAISEonline platform: cohort visualization
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Will our analytics reflect the progress that ‘Joe’ has made on so many other fronts – but not his SATs?
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Will our analytics reflect the progress that ‘Joe’ has made on so many other fronts – but not his SATs?
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?
Video conferencing analytics OU KMi’s Flashmeeting
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Video conference spoken foreign language tutorials Se
ssio
n
AV Chat AV Chat
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Mentor 1 Mentor 2
Video conferencing analytics OU KMi’s Flashmeeting
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Video conference spoken foreign language tutorials Se
ssio
n
AV Chat AV Chat
1
2
3
Mentor 1 Mentor 2
Video conferencing analytics OU KMi’s Flashmeeting
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Video conference spoken foreign language tutorials Se
ssio
n
AV Chat AV Chat
1
2
3
Mentor 1 Mentor 2
— which mentor would you want to have?...
Video conferencing analytics OU KMi’s Flashmeeting
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Video conference spoken foreign language tutorials Se
ssio
n
AV Chat AV Chat
1
2
3
Mentor 1 Mentor 2
— which mentor would you want to have?...
Mentor 1 is doing the best job: at this introductory
level, students need intensive input and
flounder if left
course completion is only one proxy for good learning
and what’s easy to
measure isn’t always what’s most important
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The Wal-Martification of education?
54 http://chronicle.com/blogs/techtherapy/2012/05/02/episode-95-learning-analytics-could-lead-to-wal-martification-of-college http://lak12.wikispaces.com/Recordings
The Wal-Martification of education?
55 http://chronicle.com/blogs/techtherapy/2012/05/02/episode-95-learning-analytics-could-lead-to-wal-martification-of-college http://lak12.wikispaces.com/Recordings
“What counts as data, how do you get it, and what does it
actually mean?”
“The basic question is not what can we measure? The basic question is
what does a good education look like?
Big questions.
“data narrowness” “instrumental learning”
“students with no curiosity”
Course completion as a proxy for learning
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http://annezelenka.com/2012/05/05/but-what-about-learning
Course completion as a proxy for learning
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http://annezelenka.com/2012/05/05/but-what-about-learning
NOTE TO SELVES: We are the HigherEd market who make it
worthwhile for major vendors to design analytics focused on
maximising course completion
PS: HEIs may feel that they are trapped by external expectations
and requirements. Systems thinking required…
let’s just pretend that learning analytics took seriously the revolution going on outside the
university front door…
we would need to devise
learning analytics for this?...
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“We 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 yet.”
“Shift Happens” http://shifthappens.wikispaces.com
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Learning analytics for this?
Learning analytics for this?
“While 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 success…”
All our Futures: Creativity, culture & education, May 1999
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“Knowledge of methods alone will not suffice: there must be the desire, the will, to employ them. This desire is an affair of personal disposition.”
John Dewey, 1933
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Dewey, J. How We Think: A Restatement of the Relation of Reflective Thinking to the Educative Process. Heath and Co, Boston, 1933
Learning analytics for this?
Learning analytics for this?
“The test of successful education is not the amount of knowledge that pupils take away from school, but their appetite to know and their capacity to learn.”
Sir Richard Livingstone, 1941
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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?
Learning analytics for this?
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Creativity, Culture and Education (2009) Changing Young Lives 2012. Newcastle: CCE. http://www.creativitycultureeducation.org/changing-young-lives-2012
Musicality ≠ Musical Reproduction
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In those early days the children were taught from the start to develop their own voice, whether literally singing, or through the
instrument they played. They were not taught music, but musicality. Central to this tuition were the partimenti, many
pages of detailed music notes which pose many questions, but leave the pupil to find the solutions. The music is not a literal transcript, which the musician reads and reproduces.
The partimenti establish, at the start, a set of rules and then pose a set of conflicts for the musician to resolve, in their own way.
http://bit.ly/U1vkNf
consider assessment for learning
(not summative assessment for
grading pupils, teachers, institutions or nations)
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Assessment for Learning
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http://assessment-reform-group.org
To what extent could automated
feedback be designed and evaluated with
emotional impact in mind?
Assessment for Learning
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http://assessment-reform-group.org
Can analytics identify proxies
for such advanced qualities?
Assessment for Learning
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http://assessment-reform-group.org
Do analytics provide constructive next
steps?
Assessment for Learning
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http://assessment-reform-group.org
How do we provide analytics feedback
that does not disempower and de-motivate struggling
learners?
analytics for…
dispositions discourse
social networks
73 See SoLAR Storm: Social Learning Analytics symposium http://www.open.ac.uk/blogs/SocialLearnResearch/2012/06/20/social-learning-analytics-symposium
Social Learning Analytics
§ Analytics focused on social learning theories, practices and platforms, e.g.
§ Discourse analytics: beyond quantitative summaries of online writing, to qualitative analysis
§ Social network analytics: visualizing effective social ties for collective learning
§ Dispositional analytics: measuring students’ readiness to engage in lifelong, lifewide learning
Ferguson R and Buckingham Shum S. (2012) Social Learning Analytics: Five Approaches. Proc. 2nd International Conference on Learning Analytics & Knowledge. Vancouver, 29 Apr-2 May: ACM Press. Eprint: http://oro.open.ac.uk/32910
Buckingham Shum, S. and Ferguson, R., Social Learning Analytics. Educational Technology & Society (Special Issue on Learning & Knowledge Analytics, Eds. G. Siemens & D. Gašević), 15, 3, (2012), 3-26. http://www.ifets.info Open Access Eprint: http://oro.open.ac.uk/34092
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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
Wei & 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 & Knowledge. Feb. 27-Mar 1, 2011, Banff. ACM Press. Eprint: http://oro.open.ac.uk/28955
Given a 2.5 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 peak…
Discourse analytics on webinar textchat
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1:04
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1:17
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1:26
1
1:32
1
1:38
1
1:44
1
1:52
1
2:03
Averag
Wei & 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 & Knowledge. Feb. 27-Mar 1, 2011, Banff. ACM Press. Eprint: http://oro.open.ac.uk/28955
Classified as “exploratory
talk”
(more substantive for learning)
“non-exploratory”
Given a 2.5 hour webinar, where in the live textchat were the most effective learning conversations?
Discourse Network Analytics = Concept Network + 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 & Knowledge (Banff, 27 Mar-1 Apr, 2011) http://oro.open.ac.uk/25829
De Liddo, A., Buckingham Shum, S., Quinto, I., Bachler, M. and Cannavacciuolo, L. Discourse-centric learning analytics. 1st International Conference on Learning Analytics & Knowledge (Banff, 27 Mar-1 Apr, 2011) http://oro.open.ac.uk/25829
KMi’s Cohere: a web deliberation platform enabling semantic social network and discourse network analytics
Rebecca is playing the role of broker,
connecting 2 peers’ contributions in meaningful ways
Analytics for C21 learning skills
Buckingham Shum, S. and Deakin Crick, R. (2012). Learning Dispositions and Transferable Competencies: Pedagogy, Modelling and Learning Analytics. Proc. 2nd Int. Conf. Learning Analytics & Knowledge. (29 Apr-2 May, Vancouver). Eprint: http://oro.open.ac.uk/32823
Different social network patterns
in different contexts may
load onto Learning
Relationships
Questioning and challenging may load onto Critical
Curiosity
Sharing relevant resources from other contexts may load onto
Meaning Making
Repeated attempts to pass
an online test may load onto
Resilience
LearningEmergence.net: embedding dispositional analytics into practice + tools EnquiryBlogger: Wordpress plugins for reflective learning journals
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Discourse analysis (Xerox Incremental Parser)
BACKGROUND KNOWLEDGE:
Recent studies indicate …
… the previously proposed …
… is universally accepted ...
NOVELTY:
... new insights provide direct evidence ...
... we suggest a new ... approach ...
... results define a novel role ...
OPEN QUESTION: … little is known … … role … has been elusive
Current data is insufficient …
GENERALIZING: ... emerging as a promising approach Our understanding ... has grown exponentially ... ... growing recognition of the
importance ...
CONRASTING IDEAS: … unorthodox view resolves … paradoxes …
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:
Ágnes Sándor & OLnet Project: http://olnet.org/node/512
De Liddo, A., Sándor, Á. 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. http://oro.open.ac.uk/31052
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
http://technologies.kmi.open.ac.uk/cohere/2012/01/09/cohere-plus-automated-rhetorical-annotation De Liddo, A., Sándor, Á. 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. http://oro.open.ac.uk/31052
Closing thoughts
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“The basic question is not what can we measure?
The basic question is what does a good education look like?
Big questions. (Gardner Campbell)
Do we value what we can measure, or measure what we really value? And just because this is tough to do, doesn’t mean we don’t do it.
(Guy Claxton, BBC Radio 4 Education Debate, Nov. 2012)
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Our analytics promote values, pedagogy and assessment regimes.
Are we clear which master
our analytics serve? Are we happy to be judged by them?
http://simon.buckinghamshum.net http://twitter.com/sbskmi