Personalization & Adaptivity

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Presented to IMS Global Conference, San Diego, 2013

Presented to IMS Global Conference, San Diego, 2013

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  • Baker, R. S. J.d., & Yacef, K. (2009). The state of educational data mining in 2009: A review and future visions. Journal of Educational Data Mining, 1(1). http://www.educationaldatamining.org/JEDM/images/articles/vol1/issue1/JEDMVol1Issue1_BakerYacef.pdf
  • Bienkowski, M., Feng, M., & Means, B. (2012). Enhancing teaching and learning through educational data mining and learning analytics. U.S. Department of Education. Retrieved on March 10, 2013 from http://www.ed.gov/edblogs/technology/files/2012/03/edm-la-brief.pdf
  • State of Human Capital, 2012, http://www.mckinsey.com/~/media/McKinsey/dotcom/client_service/Organization/PDFs/State_of_human_capital_2012.ashx
  • https://www.mckinseyquarterly.com/Economic_Studies/Productivity_Performance/Preparing_for_a_new_era_of_knowledge_work_3034
  • http://www.census.gov/prod/2013pubs/acsbr11-14.pdf
  • Rich, E. (1979). User modeling via stereotypes. Cognitive Science 3, 329-354.Fischer, G. (2001). User Modeling in Human-Computer Interactions. User Modeling and User-Adapted Interaction, 11, 65-86.
  • http://scimaps.org/maps/map/mapping_the_evolutio_81/
  • http://onlinelibrary.wiley.com/doi/10.1002/tea.3660271003/abstract (Novak)Brusilovsky, P. (2001). Adaptive hypermedia: From intelligent tutoring systems to web-based education. User Modeling and User-Adapted Interaction, 11(1-2), 87-110.
  • Anderson, J. R., Corbett, A. T., Koedinger, K. R., & Pelletier, R. (1995). Cognitive tutors: Lessons learned. The Journal of the Learning Sciences, 4(2), 167-207.Burns, H. L. (1989). Foundations of intelligent tutoring systems: An introduction. In Richardson, J. J., & Polson, M. C. (Eds.), Proceedings of the Air Force Forum for Intelligent Tutoring Systems. http://www.dtic.mil/cgi-bin/GetTRDoc?AD=ADA207096#page=16
  • http://walker.wi.gov/Images/News/6.19.12%20UW%20Flexible%20Degree%20Proposal%20Packet.pdf

Transcript

  • 1. Turning Data into PersonalizedStudent ExperiencesGeorge Siemens, PhDMay 14, 2013Presented toIMS Global
  • 2. Technique:Baker and Yacef (2009) five primary areas ofanalysis:- Prediction- Clustering- Relationship mining- Distillation of data for human judgment- Discovery with models
  • 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. 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. 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. Context
  • 7. The Conference Board& McKinsey & Co
  • 8. McKinsey Quarterly, 2012
  • 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. Increasingly: learning acrosstraditional boundaries(i.e. work, outside of classroom, hobby)
  • 11. Ok, on to adaptivity, personalization
  • 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. (btw – this isn’t new)Rich, 1979All those CMU folksFischer, 2001
  • 14. How does it work?
  • 15. First, a knowledge domain ismapped
  • 16. http://www.plosone.org/article/info:doi/10.1371/journal.pone.0004803
  • 17. http://drunks-and-lampposts.com/2012/06/13/graphing-the-history-of-philosophy/
  • 18. http://drunks-and-lampposts.com/2012/06/13/graphing-the-history-of-philosophy/
  • 19. http://linkeddata.org/
  • 20. (again, not new)Novak, 1990 (concept mapping)Semantic web: Berners-Lee, Hendler,Lassila, 2001Brusilovsky, 2001
  • 21. Next, the learner ismodeled/profiled
  • 22. Cognitive stylesCognitive modelsLearning preferences (by various criteria)Tutors (cognitive, intelligent)
  • 23. (Also, not new)Anderson, Corbett, Koedinger, Pelletier,1995That shady learning styles literatureBurns, 1989
  • 24. Knowledge domain +learner profile/knowledge +?= Personalization!The ? varies: from algorithms to pixie dust to chicken bones
  • 25. State of Wisconsin, 2012
  • 26. State of Wisconsin, 2012
  • 27. So, what about creativeprocesses?AI/ML/analytics aren’t usefulhere, are they?
  • 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. “An Ecuadorian strawberry dessert algorithmicallymaximized for pleasantness”http://www.fastcodesign.com/1672444/try-a-recipe-devised-by-ibms-supercomputer-chef
  • 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. The need to sensemake
  • 32. Sensemaking“Sensemaking is a motivated, continuouseffort to understand connections . . . in orderto anticipate their trajectories and acteffectively”(Klein et al. 2006)
  • 33. or“Sensemaking is about labeling andcategorizing to stabilize the streaming ofexperience”(Weick et al. 2005: 411)
  • 34. We socially sensemake throughstories, narratives, knowledgeexchange, discourse
  • 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. Adaptivity/Personalizationaddresses these quadrants
  • 37. The future of work is inthese quadrants
  • 38. LA interoperability
  • 39. Open Learning Analytics
  • 40. 47
  • 41. Twitter/Gmail:gsiemens