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ALT-C2012 Learning Analytics Symposium


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Confronting Reality with Big Data & Learning Analytics

We are experiencing an explosion in the quantity of data available online from archives and live streams. Learning Analytics is concerned with how educational research, and learning platform design, can make more effective use of such data (Long & Siemens, 2011). Improving outcomes through the analysis of data is of interest to researchers, administrators, systems architects, social media developers, educators and learners. Analytics are being held up by some as a way to confront, and tackle, the tough new realities of less money, less attention, and higher accountability for quality of learning.

Researchers and vendors are building reporting capabilities into tools that provide unprecedented levels of data on learners. This symposium will show what is possible, and what's coming soon. What objections could possibly be raised to such progress?

However, information infrastructure embodies and shapes worldviews: classification schemes are not only systematic ways to capture and preserve, but also to forget, by virtue of what remains invisible (Bowker & Star, 1999). Learning analytics and recommendation engines are designed with a particular conception of ‘success’, driving the patterns deemed to be evidence of progress, the interventions that are deemed appropriate, the data captured and the rules that fire in software.

This symposium will air some of the critical arguments around the limits of decontextualised data and automated analytics, which often appear reductionist in nature, failing to illuminate higher order learning. There are complex ethical issues around data fusion, and it is not clear to what extent learners are empowered, in contrast to being merely the objects of tracking technology. Educators may also find themselves at the receiving end of a new battery of institutional ‘performance indicators’ that do not reflect what they consider to be authentic learning and teaching.

This Symposium will provide the opportunity to hear a series of brief presentations introducing contrasting perspectives, before the debate is opened to all. Speakers from a cross-section of The Open University will describe how we are connecting datasets, analysing student data and prototyping next generation analytics. Complementing this, JISC will present a national capability perspective, with an update on the JISC CETIS ‘landscape analysis’ of the field, which will clarify potential benefits, issues to consider, and help institutions to assess their current capability and possible next steps.

Participants will catch up with developments in this fast moving field, through exposure to the possibilities of analytics, as well as issues to be alert to.

Published in: Education, Technology
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ALT-C2012 Learning Analytics Symposium

  1. 1. ALT-C 2012, Manchester — “A Confrontation with Reality” @sbskmi @R3beccaF @kevinmaylesSymposium: Confronting Reality with… @sheilmcnBig Data & @richardn2009Learning Analytics Buckingham Shum, Naomi Jeffery,Kevin Mayles, Richard Nurse & Rebecca FergusonThe Open University (KMI, IET, LTS & Library)Sheila MacNeillJISC CETIS
  2. 2. Symposium: Confronting Reality with…Big Data & Learning Analytics intro Simon Buckingham Shum 2
  3. 3. John Daniel 3
  4. 4. Possibly 90% of the digital data we havetoday was generated in the last 2 yearsVolume The sheer amount of data outstrips old infrastructure capacityVariety Internet of things, e-business transactions, environmental sensors,social media, audio, video, mobile…Velocity The speed of data access and analysis is explodingA quantitative shift of this scale is in fact a qualitative shift, requiringnew ways of thinking about societal phenomena 4
  5. 5. edX: “this is big data giving us the chance toask big questions about learning” 5
  6. 6. US states are getting the infrastructure in place toshare educational data across the 6
  7. 7. Analytics in your VLE:Blackboard: feedback to students 7
  8. 8. Analytics in your VLE:Desire2Learn visual analytics & predictive models Students Online tools 8
  9. 9. Adaptive platforms (ITS comes of age) generatefine-grained analytics
  10. 10. Adaptive platforms (ITS comes of age) generatefine-grained analytics
  11. 11. Purdue University SignalsReal time traffic-lights for studentsbased on a 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 CMS). Using factor analysis and logistic regression, a model was tested to predict student success based on: •  ACT or SAT score •  Overall grade-point average Predicted 66%-80% •  CMS usage composite of struggling •  CMS assessment composite students who •  CMS assignment composite needed help •  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. 12. The Wal-Martification of education? “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 “data narrowness” what does a good “instrumental learning” education look like?“students with no curiosity” Big questions. 12
  13. 13. It’s about insight and sensemaking, not data Al Essa: Learning Analytics… less data, more insight. Analytics primary task is not to report the past, but to help find the optimal path to a desired future George Siemens: Analytics doesn’t end with the data dashboard – that’s when it really starts – it’s all about sensemaking
  14. 14. Symposium: Confronting Reality with…Big Data & Learning Analytics JISC surveying the UK landscape Sheila McNeill 14
  15. 15. Confronting Big Data and Learning Analytics ALT-C 2012 Sheila MacNeill Assistant Director
  16. 16. Big data and analytics in educationn  Shift from data collecting to data connectingn  Develop data informed mind–setsn  Integration of multiple (structured and unstructured) data sourcesn  Management and use of real-time datan  (( analytics-in-education-and-learning/)
  17. 17. JISC Cetis view of the landscape Business Intelligence Learning CRM Analytics
  18. 18. practiceresearchmanagement data
  19. 19. Analytics Reconnoitren  Practical guidance of/for uses of analyticsn  Who, why, what, where, when and hown  Audience: first movers and early adoptersn  Topics: whole institutional issues, research, teaching and learning, legal and ethical issues, skills & literacies, professional development, technology and infrastructuren
  20. 20. Symposium: Confronting Reality with…Big Data & Learning Analytics VLE perspective Kevin Mayles 20
  21. 21. VLE  Analy*cs  @  the  OU   Virtual   Learning   Environment   Data   Warehouse   Usage  sta*s*cs  at  system,  faculty  and   ‘Par*cipa*on  Tracking’  func*on  to  track   module  level  –  general  paAerns   individual  students’  interac*on  with  specific   online  learning  ac*vi*es   In  pilot  2012/13  
  22. 22. Symposium: Confronting Reality with…Big Data & Learning Analytics Library perspective Richard Nurse 22
  23. 23. Learning Analytics – the Library dimension Student achievement Recommender services Library use ‘Students who looked at this article also looked at this article’ ‘Students on your course are looking at these articles’ Library Impact Data Project – Huddersfield University
  24. 24. Symposium: Confronting Reality with…Big Data & Learning Analytics social learning analytics Rebecca Ferguson 24
  25. 25. Social learning analyticsfocus on how learners build knowledgetogether in their cultural and social settings Network analytics help me identify •  People with relevant interests •  People who support my learning Discourse analytics help me locate •  Challenges and Extensions •  Evaluation and Reasoning
  26. 26. Symposium: Confronting Reality with…Big Data & Learning Analytics bringing it all together Naomi Jeffrey 26
  27. 27. Symposium: Confronting Reality with…Big Data & Learning Analytics the floor is yours… Does this excite or disturb you? Who doesn’t want education to be evidence-based and high impact? Who gets to see – and define – analytics? What does ‘good’ learning look like in an analytics dashboard? How might analytics be misinterpreted? What ethical issues arise? Your point or question… 29
  28. 28. Symposium: Confronting Reality with…Big Data & Learning Analytics join the community… 30
  29. 29. UK SoLAR “Flare” 3rd Int. Conf. Learning (national meetup) Analytics & Knowledge Mon 19 Nov LAK13, Leuven Open University 8-12 April 2013Co-sponsored by OU & JISC @SoLAResearch #LearningAnalytics @LAKconf 31