Turning Data into PersonalizedStudent ExperiencesGeorge Siemens, PhDMay 14, 2013Presented toIMS Global
Technique:Baker and Yacef (2009) five primary areas ofanalysis:- Prediction- Clustering- Relationship mining- Distillation...
Application: Bienkowski, Feng, and Means(2012)five areas of LA/EDM application:- Modeling user knowledge, behavior, and ex...
LA approach ExampleTechniquesModeling Attention metadataLearner modelingBehavior modelingUser profile developmentRelations...
LA approach ExampleApplicationsTrend Analysis and Prediction Early warning, risk identificationMeasuring impact of interve...
Context
The Conference Board& McKinsey & Co
McKinsey Quarterly, 2012
Increasing diversityof student profilesThe U.S. is now in a position when less thanhalf of students could be considered fu...
Increasingly: learning acrosstraditional boundaries(i.e. work, outside of classroom, hobby)
Ok, on to adaptivity, personalization
LA approach ExampleApplicationsTrend Analysis andPredictionEarly warning, risk identificationMeasuring impact of intervent...
(btw – this isn’t new)Rich, 1979All those CMU folksFischer, 2001
How does it work?
First, a knowledge domain ismapped
http://www.plosone.org/article/info:doi/10.1371/journal.pone.0004803
http://drunks-and-lampposts.com/2012/06/13/graphing-the-history-of-philosophy/
http://drunks-and-lampposts.com/2012/06/13/graphing-the-history-of-philosophy/
http://linkeddata.org/
(again, not new)Novak, 1990 (concept mapping)Semantic web: Berners-Lee, Hendler,Lassila, 2001Brusilovsky, 2001
Next, the learner ismodeled/profiled
Cognitive stylesCognitive modelsLearning preferences (by various criteria)Tutors (cognitive, intelligent)
(Also, not new)Anderson, Corbett, Koedinger, Pelletier,1995That shady learning styles literatureBurns, 1989
Knowledge domain +learner profile/knowledge +?= Personalization!The ? varies: from algorithms to pixie dust to chicken bones
State of Wisconsin, 2012
State of Wisconsin, 2012
So, what about creativeprocesses?AI/ML/analytics aren’t usefulhere, are they?
“We’ve been interested in pushingcomputing to a new direction, computationalcreativity. We’re trying to draw on datasets, ...
“An Ecuadorian strawberry dessert algorithmicallymaximized for pleasantness”http://www.fastcodesign.com/1672444/try-a-reci...
“For as much as $20,000 per script…a team of analystscompare the story structure and genre of a draft script withthose of ...
The need to sensemake
Sensemaking“Sensemaking is a motivated, continuouseffort to understand connections . . . in orderto anticipate their traje...
or“Sensemaking is about labeling andcategorizing to stabilize the streaming ofexperience”(Weick et al. 2005: 411)
We socially sensemake throughstories, narratives, knowledgeexchange, discourse
We turn to technical approacheswhen the data exceeds ourcapacity to create social discoursearound itBut, in fairness, once...
Adaptivity/Personalizationaddresses these quadrants
The future of work is inthese quadrants
LA interoperability
Open Learning Analytics
47
Twitter/Gmail:gsiemens
Personalization & Adaptivity
Personalization & Adaptivity
Personalization & Adaptivity
Personalization & Adaptivity
Personalization & Adaptivity
Personalization & Adaptivity
Personalization & Adaptivity
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Personalization & Adaptivity

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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
  • 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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