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LAK16 keynote Learning Analytics: Utopia or Dystopia

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Here my April 28 keynote at LAK16 in Edinburgh on Utopian and Dystopian Futures of Learning Analytics

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LAK16 keynote Learning Analytics: Utopia or Dystopia

  1. 1. Learning Analytics: Utopia or Dystopia Prof. dr. Paul A. Kirschner Distinguished University Professor Open University of the Netherlands
  2. 2. Thanks
  3. 3. Disclaimer This keynote is not about: • Privacy issues / Data protection / Big Brother • Ethical aspects / questions • Legal aspects / questions • Commercial interests / Business optimisation • Technology, Techniques, Dashboards, etcetera • … To paraphrase Bill Clinton: It’s the learning, people!
  4. 4. Your task Mr. Phelps – Mission Impossible?
  5. 5. Learning
  6. 6. National Tsing Hua Univerity Learning Sciences
  7. 7. Learning Analytics Model (Siemens, 2013) (Siemens, 2013)
  8. 8. Learning Analytics Model (Siemens, 2013) Dystopia 1: Myopic vision of what learning is
  9. 9. Learning Analytics Model (Siemens, 2013)
  10. 10. Missing link?
  11. 11. Learning Sciences theory as missing link • Variables to include in a model • Potential confounds, subgroups, or covariates in the data • Which results to attend to • Framework for interpreting results • How to make results actionable • Generalisation of results to other contexts and populations
  12. 12. Not only true of the US elections Dystopia 2: Theory Free / Theory Poor LA
  13. 13. What are we looking for?
  14. 14. Meaningful variables? Dystopia 3: Looking at wrong or invalid variables
  15. 15. Dystopia 4: Seeing Correlation as Causal
  16. 16. Spurious correlations http://tylervigen.com/spurious-correlations http://tylervigen.com/discover
  17. 17. Unintended consequences http://interactioninstitute.org/unintended-consequences/ Dystopia 5: Unintended and unwanted effects
  18. 18. Goal orientation http://buehlereducation.com/pedagogy-assessment/goal- orientation-theory-and-education/
  19. 19. Pigeonholing / Profiling / Stereotyping
  20. 20. Predict Utopia 1: Knowing what will happen (and when and why)
  21. 21. Simple data Dietz-Uhler & Hurn (2013). Using Learning Analytics to Predict (and Improve) Student Success: A Faculty Perspective
  22. 22. Adapt / Personalise? Utopia 2: Custom tailored learning and instruction
  23. 23. Learning / study strategies Summarise Highlight/underline Elaborative questions Restudy Generate keywords Distribute practise Variable practise Visualise Explain (Self) Testing Dr. Gino Camp – Welten Institute, OUNL
  24. 24. Variability of practise a‐a‐a‐b‐b‐b‐c‐c‐c‐d‐d‐d versus a‐b‐c‐d‐a‐b‐c‐d‐a‐b‐c‐d Ten steps to complex learning – Van Merriënboer & Kirschner
  25. 25. Recommend / Advise / Intervene Utopia 3: The right thing for the right learner at the right time
  26. 26. Yong Zheng, Center for Web Intelligence, DePaul University Navisro Analytics: Collaborative Filtering and Recommender Systems
  27. 27. Classification system* * Drachsler et al., Panorama of Recommender Systems to Support Learning
  28. 28. Feedback Utopia 4: Enlightening the learner
  29. 29. A better learning environment Utopia 5: Simply the best • Course improvement • Feedback for staff (instructors, tutors) • Grouping students • Planning and scheduling • Resource allocation • Etcetera
  30. 30. Multimodal data, LA ,  & dashboards for (S)SRL)   http://www.slamproject.org/blog
  31. 31. JCAL special issue “Learning analytics in massively multi-user virtual environments and courses" Available online CODE: JCALLAK16
  32. 32. paul.kirschner@ou.nl @P_A_Kirschner

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