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Social Learning Analytics


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Interactive session run at Open U, CALRG2011 Conference, 14 June 2011

Interactive session run at Open U, CALRG2011 Conference, 14 June 2011

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  • 1. CALRG 2011Social Learning AnalyticsSimon Buckingham Shum & Rebecca FergusonKnowledge Media Institute & Institute of Educational TechnologyThe Open University, Milton Keynes, UK@sbskmi / @R3beccaF 1
  • 2. How we’re going to do this...10mins Imagine.../background/critical questions – Simon15mins A taxonomy of Social Learning Analytics – Rebecca10mins SLA: more than just a bunch of techniques – Simon15mins Open discussion... 2
  • 3. Coming soon to a future near you?... Analytics Report Application from Ali Bloggs to study Z0001 This applicant has a high risk profile: 1.  No academic study for last 15 years 2.  Low socio-economic background 3.  English as a second language 4.  Weak ICT skills 5.  His responses to the learning styles survey indicate a loner, rather than a collaborative learner, known to be a disadvantage on this course [click to view the 3 other risk factors] Without a Grade 3 tutor (advanced skills in 1-1 support), based on the last 5 years data there is a 37% chance of dropping out by Week 6. [ACCEPT] [REJECT] 3
  • 4. Coming soon to a future near you?... “Hi Ann, In the last 2 weeks, it looks like you’ve been really stretching yourself. You seem to have been working on your critical thinking, with that challenge to Mike’s assumption, and the evidence-based claim about nuclear waste in your blog. Check out Donna Winter, who seems to have very different views to yours on global warming. How would you assess her position? In your next video conference tutorial, try to improve on the last three, in which you seem to have contributed only once each time.” 4
  • 5. Coming soon to a future near you?... “Did you know that two other people you know have used the Smith & Jones 2009 framework graphic? Finally, you seem to have really become a pivotal member of the Local-Global Climate Network. Good work: only a month ago you were on the edge! Why not reflect in your blog on how these groups are helping you in your long term goal to Work for the UN in Africa?” 5
  • 6. L. Johnson, R. Smith, H. Willis, A. Levine, and K. Haywood, The 2011 Horizon Report (Austin, TX: The NewMedia Consortium, 2011), 6
  • 7. Learning analytics“Learning Analytics isconcerned with thecollection, analysis andreporting of data aboutlearning in a range ofcontexts, includinginformal learning,academic institutions,and the workplace.It informs and providesinput for action tosupport and enhancelearning experiences, andthe success of learners.”2nd Int. Conf. Learning Analytics & Knowledge 2012
  • 8. “Academic Analytics”•  Stage 1—Extraction and reporting of transaction-level data•  Stage 2—Analysis and monitoring of operational performance•  Stage 3—What-if decision support “Academic analytics can be (such as scenario building) thought of as an engine to•  Stage 4—Predictive modeling and make decisions or guide simulation actions. That engine consists of five steps: capture, report,•  Stage 5—Automatic triggers of predict, act, and refine.” business processes (such as alerts)Goldstein, P. J. (2005). Academic Analytics: The Uses of Management “Administrative units, suchInformation and Technology in Higher Education: Key Findings.Boulder, Colorado: Educause Center for Applied Research as admissions and fund raising, remain the most common users of analytics in higher education today.” Campbell, J. P. & Oblinger, D.G. (2007) Academic Analytics. EDUCAUSE AcademicAnalytics/45275 8
  • 9. Academic analytics in English schools 9
  • 10. OU Analytics service: Predictivemodelling§  Probability models help us to identify patterns of success that vary between: §  student groups §  areas of curriculum §  study methods§  Previous OU study data – quantity and results – are the best predictors of future success§  The results provide a more robust comparison of module pass rates and support the OU in identifying aspects of good performance that can be shared and aspects where improvement could be realisedOU Student Statistics & Surveys Team, Institute of Educational Technology 10
  • 11. Purdue University Signals Purdues 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 CMS). Using factor analysis and logistic regression, a model was programmed 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. 11
  • 12. critical questions 12
  • 13. Pause for thought...§  in the discourse of academic analytics, there is little mention of pedagogy, theory, learning or teaching§  what models of “learning” currently underpin analytics? If we can’t log and measure it, it’s invisible...§  what learning phenomena should analytics track to equip learners for the complexities of C21?§  classification schemes are the mechanisms by which we choose not only how to remember, but also systematically forget (Bowker and Star, 1999)§  power: who is defining the measures, to what ends, and who gets to see which results? 13
  • 14. social learning analytics a taxonomy 14
  • 15. Social Learning Analytics•  Social learning network analysis•  Social learning discourse analysis•  Social learning content analysis•  Social learning dispositions analysis•  Social learning context analysis
  • 16. Social network analytics•  Networked learning uses ICT to promote connections•  Networks consist of actors (people and resources) and the ties between them.•  Ties can be classified by their frequency, quality or importance
  • 17. SNAPP •  Trace the growth of course communities •  Identify disconnected students •  Highlight the role of information brokers
  • 18. GEPHI Tony Hirst•  Networks with interconnected interests•  Interests that are shared by actors in a network•  Role of information brokers in sharing resources,•  Roles played by resources in connecting networks
  • 19. Social network analysisand social learning•  Identify and support types of interaction that promote the learning process•  Identify interventions that are likely to increase the potential of a network to support the learning of its actors
  • 20. Social learning discourse analytics•  Educational success and failure may be explained by the quality of educational dialogue, rather than simply in terms of the capability of individual students or the skill of their teachers•  The ways in which learners engage in dialogue are indicators of how they engage with other learners’ ideas, how they compare those ideas with their personal understanding, and how they account for their own point of view
  • 21. Cohere•  Annotations or discussion as a network of rhetorical moves•  Users must reflect on, and make explicit, the nature of their contribution Simon Buckingham Shum, Anna De Liddo
  • 22. Exploratory dialogue Rebecca Ferguson
  • 23. Open Mentor Denise Whitelock Analyse, visualise and compare quality of feedback
  • 24. Content analyticsAutomated methods to examine, indexand filter online media assets, with theintention of guiding learners throughthe ocean of available resources
  • 25. LOCOanalystProvides feedback for content authors and teachers that can helpthem to improve their online courses (Jovanovic et al., 2008)
  • 26. Visual search Suzanne LittleVisual similarity search uses features of images such as colour,texture and shape in order to find material that is visually related
  • 27. iSpot Social content analytics draw upon the tags, ratings and additional data supplied by learners
  • 28. Social learning dispositions analytics•  Learning dispositions provide a way of identifying and naming the qualities of a good learner.•  They comprise the seven dimensions of ‘learning power’: changing & learning, critical curiosity, meaning making, dependence & fragility, creativity, relationships/ interdependence and strategic awareness•  Dynamic assessment of learning power can be used to reflect back to learners what they say about themselves in relation to these dimensions Ruth Deakin Crick, University of Bristol
  • 29. ELLI•  Effective Lifelong Learning Inventory (ELLI) responses produce a learning profile•  This profile forms the basis for a mentored discussion with the potential to spark and encourage changes in the learner’s activities, attitude and approach to learning
  • 30. ELLImentThomas Ullmann:
  • 31. EnquiryBloggerRebecca Ferguson, Simon Buckingham Shum, Ruth Deakin Crick
  • 32. Social learning context analyticsTaking context into account:•  Formal settings•  Informal settings•  Mobile learning•  Synchronous environments•  Asynchronous environments
  • 33. Identifying and using context My OU Story: Liam Green Hughes Stuart Brown Tony Hirst
  • 34. Affinity groups
  • 35. reflections... 36
  • 36. Tectonic shifts in the learning landscape...TECH: online, personalised, realtime, multimedia, mobile... Taken together, these are profound shifts in power,FREE/OPEN: expected initially: I’ll relationships,pay if it’s good enough economics...SOCIAL LEARNING: innovation nowdepends on itVALUES:autonomy, diversity, self-expression, participation if these reshapePOST-INDUSTRIAL: new institutional our conception of theroles in post-industrial education future of learningsystem – do they not also reshape our conception of the future of learning analytics? 37
  • 37. Tectonic shifts in the learning landscape... The emerging “2.0” landscapes for learning, e.g. social scholarship and capital, critical knowledge work demand thinking, new, more meaningful citizenship, indicators than habits of mind, conventional BI/MIS resilience, collaboration skills, creativity, emotional intelligence… 38
  • 38. SLA: it’s not just what they do (taxonomy)but how we use them (credibility/integrity) Analytics should step beyond the C20 business Beyond a tool for intelligence mindset institutions to track (cf. C21 “pervasive BI”) learners, these are tools to place in the hands of those being tracked Concerns about the abuse of SLA are about helping analytics may rest on the old people to grow as power configuration of an learners through institutionally wielded personal + collective instrument, to gather formative feedback summative data 39
  • 39. Some links...Learning Analytics blog, resources & open coursehttp://www.learninganalytics.net2nd Int. Conf. Learning Analytics, Vancouver, Apr 2012http://lak12.sites.olt.ubc.caKMi Learning Analytics R&D 40