Your SlideShare is downloading. ×

Learning analytics : foundation of mass personalized education

739

Published on

Published in: Education, Technology
0 Comments
1 Like
Statistics
Notes
  • Be the first to comment

No Downloads
Views
Total Views
739
On Slideshare
0
From Embeds
0
Number of Embeds
2
Actions
Shares
0
Downloads
9
Comments
0
Likes
1
Embeds 0
No embeds

Report content
Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

Cancel
No notes for slide

Transcript

  • 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
  • 10. http://www.wearethepeoplemovie.com
  • 11. SchoolSociety19th21st
  • 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
  • 28. LearningAnalyticsEducationalDataminingAcademicAnalytics
  • 29. Siemens, G., & Long, P. (2011). Penetrating the fog: Analyticsin learning and education. Educause Review, 46(5), 30-32.
  • 30. LearnersDataAnalysisInterventionThe Learning Analytics cycle
  • 31. LearnersDataThe Learning Analytics cycle
  • 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
  • 33. LearnersDataAnalysisThe Learning Analytics cycle
  • 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
  • 35. LearnersDataAnalysisInterventionThe Learning Analytics cycle
  • 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
  • 39. LearnersDataAnalysisInterventionThe Learning Analytics cycle
  • 40. What are potential issues or downsides?Critical thinking
  • 41. PrivacyLack of dataPedagogical effectJumping to conclusionsEnforcing the factory modelTool-centric vs. Learner-centricPotential issues
  • 42. What could possible scenarios be?The future of learning
  • 43. Are droids taking our jobs?
  • 44. Official sources of information
  • 45. People that inspired meSir Ken Robinson Noam Chomsky Eric MazurGeorge Siemens Erik Duval Salman Khan
  • 46. Im not saying everythingshould be changedIm saying everythingshould be questioned
  • 47. Thank youhttp://www.slideshare.net/sanderlatour

×