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Personalization & Adaptivity

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Presented to IMS Global Conference, San Diego, 2013

Published in: Education, Technology

Personalization & Adaptivity

  1. 1. Turning Data into PersonalizedStudent ExperiencesGeorge Siemens, PhDMay 14, 2013Presented toIMS Global
  2. 2. Technique:Baker and Yacef (2009) five primary areas ofanalysis:- Prediction- Clustering- Relationship mining- Distillation of data for human judgment- Discovery with models
  3. 3. Application: Bienkowski, Feng, and Means(2012)five areas of LA/EDM application:- Modeling user knowledge, behavior, and experience- Creating profiles of users- Modeling knowledge domains- Trend analysis- Personalization and adaptation
  4. 4. LA approach ExampleTechniquesModeling Attention metadataLearner modelingBehavior modelingUser profile developmentRelationship Mining Discourse analysisSentiment analysisA/B TestingNeural networksKnowledge Domain Modeling Natural language processingOntology developmentAssessment (matching user knowledge withknowledge domain)Siemens 2013: Adapted from Bienkowski et al, 2012, Baker & Yacef, 2009, Baker & Siemens 2013
  5. 5. LA approach ExampleApplicationsTrend Analysis and Prediction Early warning, risk identificationMeasuring impact of interventionsChanges in learner behavior, course discussions,identification of error propagationPersonalization/AdaptivelearningRecommendations: content and socialconnectionsAdaptive content provision to learnersAttention metadataStructural analysis Social network analysisLatent semantic analysisInformation flow analysisSiemens 2013: Adapted from Bienkowski et al, 2012, Baker & Yacef, 2009, Baker & Siemens 2013
  6. 6. Context
  7. 7. The Conference Board& McKinsey & Co
  8. 8. McKinsey Quarterly, 2012
  9. 9. Increasing diversityof student profilesThe U.S. is now in a position when less thanhalf of students could be considered fulltimestudents. In other words, students who canattend campus five days a week nine-to-five,are now a minority.(Bates, 2013)
  10. 10. Increasingly: learning acrosstraditional boundaries(i.e. work, outside of classroom, hobby)
  11. 11. Ok, on to adaptivity, personalization
  12. 12. LA approach ExampleApplicationsTrend Analysis andPredictionEarly warning, risk identificationMeasuring impact of interventionsChanges in learner behavior, course discussions,identification of error propagationPersonalization/AdaptivelearningRecommendations: content and socialconnectionsAdaptive content provision to learnersAttention metadataStructural analysis Social network analysisLatent semantic analysisInformation flow analysisSiemens 2013: Adapted from Bienkowski et al, 2012, Baker & Yacef, 2009, Baker & Siemens 2013Personalization as the holy grail of learning
  13. 13. (btw – this isn’t new)Rich, 1979All those CMU folksFischer, 2001
  14. 14. How does it work?
  15. 15. First, a knowledge domain ismapped
  16. 16. http://www.plosone.org/article/info:doi/10.1371/journal.pone.0004803
  17. 17. http://drunks-and-lampposts.com/2012/06/13/graphing-the-history-of-philosophy/
  18. 18. http://drunks-and-lampposts.com/2012/06/13/graphing-the-history-of-philosophy/
  19. 19. http://linkeddata.org/
  20. 20. (again, not new)Novak, 1990 (concept mapping)Semantic web: Berners-Lee, Hendler,Lassila, 2001Brusilovsky, 2001
  21. 21. Next, the learner ismodeled/profiled
  22. 22. Cognitive stylesCognitive modelsLearning preferences (by various criteria)Tutors (cognitive, intelligent)
  23. 23. (Also, not new)Anderson, Corbett, Koedinger, Pelletier,1995That shady learning styles literatureBurns, 1989
  24. 24. Knowledge domain +learner profile/knowledge +?= Personalization!The ? varies: from algorithms to pixie dust to chicken bones
  25. 25. State of Wisconsin, 2012
  26. 26. State of Wisconsin, 2012
  27. 27. So, what about creativeprocesses?AI/ML/analytics aren’t usefulhere, are they?
  28. 28. “We’ve been interested in pushingcomputing to a new direction, computationalcreativity. We’re trying to draw on datasets, not just to make inferences about theworld, but to create new things you’venever seen”Lav Varshney on Watsonhttp://www.fastcodesign.com/1672444/try-a-recipe-devised-by-ibms-supercomputer-chef
  29. 29. “An Ecuadorian strawberry dessert algorithmicallymaximized for pleasantness”http://www.fastcodesign.com/1672444/try-a-recipe-devised-by-ibms-supercomputer-chef
  30. 30. “For as much as $20,000 per script…a team of analystscompare the story structure and genre of a draft script withthose of released movies, looking for clues to box-officesuccess.”
  31. 31. The need to sensemake
  32. 32. Sensemaking“Sensemaking is a motivated, continuouseffort to understand connections . . . in orderto anticipate their trajectories and acteffectively”(Klein et al. 2006)
  33. 33. or“Sensemaking is about labeling andcategorizing to stabilize the streaming ofexperience”(Weick et al. 2005: 411)
  34. 34. We socially sensemake throughstories, narratives, knowledgeexchange, discourse
  35. 35. We turn to technical approacheswhen the data exceeds ourcapacity to create social discoursearound itBut, in fairness, once we technically sensemake, we turn to narrative to share
  36. 36. Adaptivity/Personalizationaddresses these quadrants
  37. 37. The future of work is inthese quadrants
  38. 38. LA interoperability
  39. 39. Open Learning Analytics
  40. 40. 47
  41. 41. Twitter/Gmail:gsiemens

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