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

1. Learning Analytics: Utopia or Dystopia
Prof. dr. Paul A. Kirschner
Distinguished University Professor
Open University of the Netherlands

2. Thanks

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!

11. Your task Mr. Phelps – Mission Impossible?

12. Learning

14. National Tsing Hua Univerity
Learning
Sciences

16. Learning Analytics Model (Siemens, 2013)
(Siemens, 2013)

19. Learning Analytics Model
(Siemens, 2013)
Dystopia 1: Myopic vision of what learning is

20. Learning Analytics Model
(Siemens, 2013)

21. Missing link?

22. 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

24. Not only true of the US elections
Dystopia 2: Theory Free / Theory Poor LA

25. What are we looking for?

26. Meaningful variables?
Dystopia 3: Looking at wrong or invalid variables

27. Dystopia 4: Seeing Correlation as Causal

28. Spurious correlations
http://tylervigen.com/spurious-correlations
http://tylervigen.com/discover

31. Unintended consequences
http://interactioninstitute.org/unintended-consequences/
Dystopia 5: Unintended and unwanted effects

32. Goal orientation
http://buehlereducation.com/pedagogy-assessment/goal-
orientation-theory-and-education/

33. Pigeonholing / Profiling / Stereotyping

39. Predict
Utopia 1: Knowing what will happen (and when and why)

40. Simple data
Dietz-Uhler & Hurn (2013). Using Learning Analytics to Predict
(and Improve) Student Success: A Faculty Perspective

42. Adapt / Personalise?
Utopia 2: Custom tailored learning and instruction

43. 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

44. 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

47. Recommend / Advise / Intervene
Utopia 3: The right thing for the right learner at the right time

48. Yong Zheng, Center for Web Intelligence, DePaul University
Navisro Analytics: Collaborative Filtering and Recommender Systems

49. Classification system*
* Drachsler et al., Panorama of Recommender Systems to Support Learning

50. Feedback
Utopia 4: Enlightening the learner

52. A better learning environment
Utopia 5: Simply the best
• Course improvement
• Feedback for staff (instructors, tutors)
• Grouping students
• Planning and scheduling
• Resource allocation
• Etcetera

54. Multimodal data, LA ,
&
dashboards for (S)SRL)
http://www.slamproject.org/blog

57. JCAL special issue
“Learning analytics in
massively multi-user
virtual environments and
courses"
Available online
CODE: JCALLAK16

58. paul.kirschner@ou.nl
@P_A_Kirschner