ico-fallschool2012-learninganalyticswkshp

Learning Analytics Workshop (8-9 Nov. 2012)

ICO Fall School 2012, Santuari de Santa Maria del Collell, Girona https://sites.google.com/site/icofallschool2012

Knowledge Media Institute

@sbskmi linkedin.com/in/simon

Simon Buckingham Shum Knowledge Media Institute, The Open University UK simon.buckinghamshum.net

What does ‘the cloud’ know about you?

The plan today

1.  Introductions + intro lecture 2.  designing your own analytics v1

Introducing a new Analytics Platform…

From a recent review…

“Some have tried to argue that this technology doesn't work out cost effectively when compared to conventional tests... but this misses a huge point. More often than not, we test after the event and discover the problem — but this is too late..”

Aquarium Analytics!

How is your aquatic ecosystem?

“This means that the keeper can be notified before water conditions directly harm the fish—an assured outcome of predictive software that lets you know if it looks like the pH is due to drop, or the temperature is on its way up.

This way, it’s a real fish saver, as opposed to a forensic examiner, post-wipeout.”

(From a review of Seneye, in a hobbyist magazine) 8

How is your learning ecosystem?

This means that the teacher can be notified before learning conditions directly harm the students — an assured outcome of predictive software that lets you know if it looks like engagement is due to drop, or attainment is on its way up.

This way, it’s a real student saver, as opposed to a forensic examiner, post-wipeout.

…but when it’s done calibrating and the dashboard springs to life, there’s an exciting sense of control

– BUT you still need to know what ‘good’ looks like

First-to-market immaturity, tricky install process…

is education poised to become a data-driven enterprise and science

Possibly 90% of the digital data we have today was generated in the last 2 years

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”

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

http://www.mirror-project.eu

http://quantifiedself.com/guide

Educational Data Mining research community

Learning Analytics research community

What do we mean by Learning Analytics?

Learning analytics

“Learning Analytics is concerned with the collection, analysis and reporting of data about learning in a range of contexts, including informal learning, academic institutions, and the workplace. It informs and provides input for action to support and enhance learning experiences, and the success of learners.”

2nd Int. Conf. Learning Analytics & Knowledge 2012

A learning analytics ecosystem

learners

educators

learners

educators

learning analytics data collection cycle

Analytics cycle (Doug Clow) h"p://www.slideshare.net/dougclow/the-‐learning-‐analy7cs-‐cycle-‐closing-‐the-‐loop-‐effec7vely (slide 5)

Analytics cycle (George Siemens) h"p://www.slideshare.net/gsiemens/eli-‐2012-‐sensemaking-‐analy7cs (slide 7)

?!*?!*

learners

educators

?!*?!*

learners

educators

data curators/ translators

dashboard design team

Where did the data come from?

learners

researchers / educators / instructional designers

theories pedagogies

assessments tools

learners

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

learners

theories pedagogies

assessments tools

gn feedback

intent

outcome

Optimize the system for what?

Same outcomes, but higher scores?

Learning Analytics as

Evolutionary Technology

• more engaging • better assessed • better outcomes

• deliverable at scale

New outcomes we couldn’t assess before?

Learning Analytics as

Revolutionary Technology

• learner behaviours quantifiable • interpersonal networks quantifiable

• discourse quantifiable • moods and dispositions quantifiable

different levels of analytic

‘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 34

Macro/Meso/Micro Learning Analytics

Macro: region/state/national/international

Meso: institution-wide

Micro: individual user actions

(and hence cohort)

US states are getting the infrastructure in place

dataqualitycampaign.org

National league tables for English schools

Analytics-savvy Leaders are the future?

42 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

43 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

…but do they know anything about the roles that language plays in

learning and knowledge construction?

BI+HigherEd communities of practice

Business Intelligence

≠ Learning Analytics

(and hence cohort)

Analytics in your VLE: Blackboard: feedback to students

http://www.blackboard.com/Platforms/Analytics/Overview.aspx

Socrato: train for SATs

49 http://www.socrato.com

Khan Academy: more data to teachers, finer-grained feedback to students

50 http://www.thegatesnotes.com/Topics/Education/Sal-Khan-Analytics-Khan-Academy

https://grockit.com/research

Adaptive platforms generate fine-grained analytics

http://knewton.com

Adaptive platforms generate fine-grained analytics

http://oli.cmu.edu

Purdue University Signals: real time traffic-lights for students based on predictive model

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

http://www.desire2learn.com/products/analytics

Desire2Learn visual analytics & predictive models which can be interrogated on different dimensions

http://www.desire2learn.com/products/analytics

The VLE—BI convergence

(and hence cohort)

Hard distinctions between Learning + Academic analytics may dissolve

Aggregation of user traces enriches meso + macro analytics with finer-grained process data

…as they get joined up, each level enriches the others

(and hence cohort)

Hard distinctions between Learning + Academic analytics may dissolve

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?

wrong.

a very healthy debate is brewing…

data (indeed technology)

is not neutral

data does not wholly ‘speak for itself’

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

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

course completion is only one proxy for good learning

and what’s easy to

measure isn’t always what’s most important

The Wal-Martification of education?

66 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?

67 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”

context

Video conferencing analytics OU KMi’s Flashmeeting

Video conference spoken foreign language tutorials Se

AV Chat AV Chat

Mentor 1 Mentor 2

— which mentor would you want to have?...

AV Chat AV Chat

Mentor 1 Mentor 2

AV Chat AV Chat

Mentor 1 Mentor 2

AV Chat AV Chat

Mentor 1 Mentor 2

Mentor 1 is doing the best job: at this introductory

level, students need intensive input and

flounder if left

context

Learning analytics in English schools

Will our analytics reflect the progress that ‘Joe’ has made on so many other fronts – but not his sats?

let’s just pretend that learning analytics took seriously the revolution going on outside the

university front door…

We need to devise learning

analytics for this?...

“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

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

“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

Dewey, J. How We Think: A Restatement of the Relation of Reflective Thinking to the Educative Process. Heath and Co, Boston, 1933

“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

Expert-led enquiry

Student-led enquiry

Expert-led teaching

Student-led revision

Teacher agency Student agency

Repetition, Abstraction Acquisition

Authenticity Agency Identity

Teaching as learning design

The Knowledge-Agency Window

consider assessment for learning

(not summative assessment for

grading pupils, teachers, institutions or nations)

Assessment for Learning

http://assessment-reform-group.org

To what extent could automated

feedback be designed and evaluated with

emotional impact in mind?

Can analytics identify proxies

for such advanced qualities?

Do analytics provide constructive next

steps?

How do we provide analytics feedback

that does not disempower and de-motivate struggling

learners?

analytics for…

dispositions discourse

social networks

90 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

Socio-cultural discourse analysis (Mercer et al, OU)

•  Disputational talk, characterised by disagreement and individualised decision making.

•  Cumulative talk, in which speakers build positively but uncritically on what the others have said.

•  Exploratory talk, in which partners engage critically but constructively with each other's ideas.

92 Mercer, N. (2004). Sociocultural discourse analysis: analysing classroom talk as a social mode of thinking. Journal of Applied Linguistics, 1(2), 137-168.

•  Exploratory talk, in which partners engage critically but constructively with each other's ideas.

•  Statements and suggestions are offered for joint consideration.

•  These may be challenged and counter-challenged, but challenges are justified and alternative hypotheses are offered.

•  Partners all actively participate and opinions are sought and considered before decisions are jointly made.

•  Compared with the other two types, in Exploratory talk knowledge is made more publicly accountable and reasoning is more visible in the talk.

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

Elluminate sessions can be very long – 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 & Knowledge (Banff, Canada, 27 Mar-1 Apr, 2011)

Defining indicators of Exploratory Talk

Category Indicator Challenge But if, have to respond, my view Critique However, I’m 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, that’s 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

Time Contribution 2:42 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.

2:42 PM Thanks, that's great! I am sure I understood everything, but looks inspiring!

2:43 PM Yes why OU tools not generic tools?

2:43 PM Issues of interoperability

2:43 PM The "new" SocialLearn site looks a lot like a corkboard where you can add various widgets, similar to those existing web start pages.

2:43 PM What if we end up with as many apps/gadgets as we have social networks and then we need a recommender for the apps!

2:43 PM My question was on the definition of the crowd in the wisdom of crowds we acsess in the service model?

2:43 PM there are various different flavours of widget e.g. Google gadgets, W3C widgets etc. SocialLearn has gone for Google gadgets

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 & 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

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?

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 scholarly writing

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

analytics for reflecting on “networked expertise”

(a key skill for our times)

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 & Knowledge (Banff, 27 Mar-1 Apr, 2011) http://oro.open.ac.uk/25829

Visualizing and filtering social ties in SocialLearn by topic and type

Visualising Social Learning in the SocialLearn Environment. Bieke Schreurs and Maarten de Laat (Open University, The Netherlands), Chris Teplovs (Problemshift Inc. and University of Windsor), Rebecca Ferguson and Simon Buckingham Shum (Open University UK), SoLAR Storm webinar, Open University UK. http://bit.ly/UaFhbL

Dispositional Learning Analytics

Dispositions are beginning to register within the learning analytics community

113 Brown, M., Learning Analytics: Moving from Concept to Practice. EDUCAUSE Learning Initiative Briefing, 2012. http://www.educause.edu/library/resources/learning-analytics-moving-concept-practice

In your experience, what are the qualities shown by the most effective learners?

Think about the most effective learners you’ve met/mentored/taught

Not necessarily the highest grade scorers, but the ones

who showed a sustained appetite for learning

What qualities/dispositions/attitudes did they bring?

A ‘visual learning analytic’ 7-dimensional spider diagram of how the learner sees themself

115 Bristol and Open University are now embedding ELLI in learning software.

Basis for a mentored-discussion on how the

learner sees him/herself, and strategies for

strengthening the profile

ELLI: Effective Lifelong Learning Inventory Web questionnaire 72 items (children and adult versions: used in schools, universities and workplace)

Validated as loading onto 7 dimensions of “Learning Power”

Changing & Learning

Meaning Making

Critical Curiosity

Creativity

Learning Relationships

Strategic Awareness

Resilience

Being Stuck & Static

Data Accumulation

Passivity

Being Rule Bound

Isolation & Dependence

Being Robotic

Fragility & Dependence

Univ. Bristol and Vital Partnerships provides practitioner resources and tools to support their application in schools and the workplace 117

Learning to Learn: 7 Dimensions of Learning Power Factor analysis of the literature plus expert interviews: identified seven dimensions of effective “learning power”, since validated empirically with learners at many levels. (Deakin Crick, Broadfoot and Claxton, 2004)

Datasets: >40,000 ELLI profiles

(data from other hosted apps)

Learning Warehouse 2.0 analytics platform

Analytics: Real time ELLI Analytics reports

Bespoke research reports

User experience: Research-validated assessment tools

Researcher interface Learning Communities

Adding imagery to ELLI dimensions to connect with learner identity

Working with Gappuwiyak School, N. Territory AUS (Ruth Deakin Crick, University of Bristol) http://bit.ly/srUSHE

Strategic Awareness: Emu - Wurrpan

Changing & Learning: The Drongo - Guwak

Learning Relationships: The Cockatoo - Ngerrk

Meaning Making: The Pigeon - Nabalawal

Critical Curiosity: Sea Eagle - Djert

Resilience: Brolga - Gudurrku

Creativity: Bower Bird - Djurwirr

Cohort analytics for educators and organizational leaders

123 123

Plugin visualizes blog categories,

mirroring the ELLI spider

EnquiryBlogger: Tuning Wordpress as an ELLI-based learning journal

Standard Wordpress editor

Categories from ELLI

Primary School EnquiryBloggers Bushfield School, Wolverton, UK

EnquiryBlogger: blogging for Learning Power & Authentic Enquiry http://learningemergence.net/2012/06/20/enquiryblogger-for-learning-power-authentic-enquiry

EnquiryBlogger dashboard

Could a platform generate an ELLI profile from user traces?

Shaofu Huang: Prototyping Learning Power Modelling in SocialLearn http://www.open.ac.uk/blogs/SocialLearnResearch/2012/06/20/social-learning-analytics-symposium

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

SocialLearn provides new possibilities of looking at learners learning

ELLI works from what learners say they do

Now we can observe what they actually do…

128 Shaofu Huang: Prototyping Learning Power Modelling in SocialLearn http://www.open.ac.uk/blogs/SocialLearnResearch/2012/06/20/social-learning-analytics-symposium

Mentored discussions

Educator or leader’s interventions

ELLI feedbacks inform development of learning

Shaofu Huang: Prototyping Learning Power Modelling in SocialLearn http://www.open.ac.uk/blogs/SocialLearnResearch/2012/06/20/social-learning-analytics-symposium

Your most recent mood comment: “Great, at last I have found all the resources that I have been looking for, thanks to"Steve and Ellen."

In your last discussion with your mentor, you decided to work on your resilience by taking on more learning challenges

Your ELLI Spider shows that you have made a start on working on your resilience, and that you are also beginning to work on your creativity, which you identified as another area to work on.

Based on: Buckingham Shum, S. and Ferguson, R. (2011). Social Learning Analytics. Available as: Technical Report KMI-11-01, Knowledge Media Institute, The Open University, UK. http://kmi.open.ac.uk/publications/pdf/kmi-11-01.pdf

Dream? Student’s analytics dashboard

Closing thoughts

“The basic question is not what can we measure?

The basic question is

what does a good education look like?”

(Gardner Campbell)

http://chronicle.com/blogs/techtherapy/2012/05/02/episode-95-learning-analytics-could-lead-to-wal-martification-of-college http://lak12.wikispaces.com/Recordings

Will learning analytics merely turbocharge the current educational paradigm?

— which is so often declared

not fit for purpose…

…or will learning analytics reflect what we now know about designing authentic,

engaged learning, developing the new qualities that a

complex society demands?

Learning Analytics is becoming a new discipline and research field

www.SoLAResearch.org Follow: @SoLAResearch

Learning Analytics conference April 2012, Leuven: lakconference.org

Invent your own Analytics cycle based on your research interests…

What kinds of learners? What kinds of learning?

What data could be captured digitally in

the use context?

What data patterns might be proxies for good/poor learning?

What human +/or software interventions

might be triggered?

Learning Analytics workshop

day 2 plan

1.  Post-it affinity mapping 2.  Team dashboard design 3.  Plenary presentations 4.  LAnoirblanc photo shoot 5.  Sharing images + stories

What are we interested in? (Affinity Mapping exercise)

Subject e.g. maths

argumentation essay structure social networks

dispositions Data

e.g. discourse graphical

video user logs

survey Pedagogy + Context e.g. face-face special needs constructivist

write 1 post-it per interest

Subject e.g. maths

argumentation essay structure social networks

dispositions

Focus e.g. maths

reading essay structure

dispositions argumentation Data

e.g. discourse graphical

video user logs

survey

Data e.g. discourse

social ties essays

user logs survey Pedagogy

+ Context e.g. face-face special needs constructivist

Pedagogy + Context e.g. face-face special needs constructivist

DIY Analytics Elaborated version of figure from Doug Clow: h"p://www.slideshare.net/dougclow/the-‐learning-‐analy7cs-‐cycle-‐closing-‐the-‐loop-‐effec7vely (slide 5)

What kinds of learners? What kinds of learning?

What data could be generated digitally

from the use context? (you can invent future technologies if need)

Does your theory predict patterns

signifying learning?

What human +/or software

interventions /recommendations?

How to render the analytics, for whom, and will they

understand them?

What analytical tools could be used to find

such patterns?

ethics purpose

design your own analytics dashboard

LAnoirblanc reactions to Learning Analytics in image and story

LAnoirblanc.tumblr.com Choose an image and email it to the site with your story…

Emailing your photo…

LAnoirblanc Add your photo and story to the website

1.  Take a photo or choose an image from the web

2.  Email it + your tags, and a story or comments: To: semtaur2@tumblr.com

Subject: (no title needed) Message: #dream #nightmare #fairydust

#yourtag #yourtag (choose your tags: each must be a single word)

text of your story... (add your name if you wish)

Attachment: the photo

3.  It will appear on LAnoirblanc.tumblr.com

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