1. Learning Analyticsfoundation of mass personalized educationSander Latour
2. Bedankt voor de uitnodigingThank you for inviting me
3. Roadmap to the beerThe educational modelThe revolutionThe technologyLearning AnalyticsThe future of learning
4. Roadmap to the beerPoint out the irony of this talkWalk out and get a beerAlternative
5. What characterizes ourmodel of education?High school University
6. Traditional educationTeacher-centricOne-size fits allStandardizedPassiveIndividuallyAssessment-centricCertain ordering of topics
7. Does the system work?Did it work for you? For your friends?For everybody in society?
8. Revolution in education"the world of education is stirring"
9. Concerns on the surface
12. Most watched?
13. Sir Ken Robinson
14. Big Breakthroughs HappenWhen What Is SuddenlyPossible Meets What IsDesperately Necessary- Thomas L. Friedman
15. Or: Old Wine In New Bottles
16. technicallySo what is suddenly possible?
17. There are many developmentsthat improve educationwithout (explicitly) using technologyWait a minute
18. Online learningTransformation or Digitalization?
19. Massive Open Online CourseEver heard of MOOCs?
20. Not all Online Learning......is massive, open or even a course
21. Web-based ITS MOOC
22. Web-based ITS MOOC
23. Learning Analyticsfoundation of mass personalized education
24. Many studentsIncreasing dataHow can you personalize?
25. Learning Analytics is the measurement, collection,analysis and reporting of data about learners and theircontexts, for purposes of understanding and optimizinglearning and the environment in which it occurs.
26. LearnersDataAnalysisInterventionThe Learning Analytics cycleDoug Clow. 2012. The learning analytics cycle: closing the loop effectively.In Proceedings of the 2nd International Conference on Learning Analytics and Knowledge (LAK 12)
27. Arnold, K. E. (2010). Signals: Applying Academic Analytics.Educause Quarterly, 33(1), n1Purdue Course Signals
32. Browsing behaviorChat/forum/blog postsComments on chat/forum/blog postsLikes/ratingsAnswers/mistakes in quizzesTime spent on taskRequesting hintsSharing of resourcesSocial interactionsDrafts of end productsLocationBody sensorsEnvironment/contextVideo recording of the learningPossible data sources
34. Recommendations for materialRecommendations for pupils / mentorsPrediction of students at riskFinding bottlenecks in contentUpdating cognitive / user modelsAnalysing video control behaviorSocial Network AnalysisDiscourse AnalyticsHead pose trackingTracking speech at a tableTypical analysis
36. ReflectionShowing a prediction of successOverview of what you didOverview of what the group didInsight in the group processSuggestionLearning materialIntelligent CurriculumPotential partnersPotential mentorsBetter learning approachIntervention via student
37. ReflectionShowing a prediction of successOverview of what a student didOverview of what the group didInsight in the group processSuggestionNew learning materialPotential partnersPotential mentorsBetter teaching approachIntervention via teacher
38. Adaptive learning materialAdaptive navigationAdaptive testsClean-up of bad materialAutomatic intervention