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Harvardx talk Presentation Transcript

  • 1. Activating Latent Knowledge Dragan Gasevic George Siemens edX July 3, 2014
  • 2. Agenda 1. Latent Capacity 2. Legacy assumptions 3. cMOOCs 4. Future directions (lessons)
  • 3. Google’s technology infrastructure
  • 4. Occupy Wall Street
  • 5. Arab Spring
  • 6. What are the tools?
  • 7. 100 people in a room theory of knowledge
  • 8. The power of integration…
  • 9. Education is waiting for its latency activating tools
  • 10. What is required Making transparent what we know (declaring knowledge, explicit or mined) Creating a persistence and progressive identity (knowledge map)
  • 11. Agenda 1. Latent Capacity 2. Legacy assumptions 3. cMOOCs 4. Future directions (lessons)
  • 12. Legacy traces: Assumptions that need to change Grading Teaching practices Education theories Networked models of learning
  • 13. Agenda 1. Latent Capacity 2. Legacy assumptions 3. cMOOCs 4. Future directions (lessons)
  • 14. Who/what influences information flows in a cMOOC?
  • 15. What/who drives community formation in a cMOOC?
  • 16. CCK11 – student demographics
  • 17. Socio-technical approach to network analysis
  • 18. Most active participants Node W1 W5 W6 W12 Description Domain @cck11feeds 0 282 447 1160 Course Aggregator N/A @web20education 0 117 147 929 European Teacher Secondary School @profesortbaker 0 281 330 404 South American English Teacher Higher Education @smoky_stu 0 46 82 306 Australian IT Teacher Secondary School @pipcleaves 23 128 139 208 Australian Educational Consultant Entrepreneurship @vanessavaile 0 77 86 196 Social Media Content Curator Higher Education @profesorbaker 0 121 136 147 South American English Teacher Languages @shellterrell 0 105 133 146 North American English Teacher Entrepreneurship @blog4edu 0 100 128 141 International Organization Various @suifaijohnmak 0 63 69 134 Australian Teacher of Logistics Higher Education Distribution of weighted output degree for weeks 1, 5, 6, and 12 with the demographic data for the top 10 ranked nodes within the last week
  • 19. Most influential nodes Distribution of weighted input degree for weeks 1, 5, 6, and 12, for the top 10 ranked nodes within the last week Node W1 W5 W6 W12 #cck11 29 861 1052 1982 #edchat 0 224 268 454 #eltchat 0 213 270 320 @profesortbaker 0 127 160 174 #edtech20 0 17 24 161 #edtech 0 60 72 154 #elearning 0 25 26 145 #education 0 54 62 110 #connectivism 2 27 31 100 #eadsunday 6 34 51 89
  • 20. Network authorities Variation of the authority weights for the top ranked social and technological nodes, over the 12 weeks of the course
  • 21. Network authorities Variation of the authority weights for the top ranked social nodes, over the 12 weeks of the course
  • 22. Network hubs Variation of the hub weights for the top ranked nodes, over the 12 weeks of the course
  • 23. Network brokers Variation of the betweenness centrality values for the top ranked nodes, over the 12 weeks of the course
  • 24. Network centers Variation of the input closeness centrality values for the top ranked nodes, over the 12 weeks of the course
  • 25. Community formation Network modularity 19 communities identified Newman, M. E. (2006). Modularity and community structure in networks. Proceedings of the National Academy of Sciences, 103(23), 8577–8582.
  • 26. Community formation 26%
  • 27. Community formation 26% 25%
  • 28. Community formation 12%
  • 29. Community formation 12% 9%
  • 30. Agenda 1. Latent Capacity 2. Legacy assumptions 3. cMOOCs 4. Future directions (lessons)
  • 31. PKG Capturing, mining, inferring what a learner knows
  • 32. CB 1. PKG 2. Granularization of learning content. 3. Match PKG with knowledge of a field. 4. ?? 5. Fill gaps 6. Get recognized
  • 33. Implications on/of technology design
  • 34. Pedagogy vs. technology
  • 35. Pedagogy vs. technology Lou, Y., et al. (2006). Media and pedagogy in undergraduate distance education: A theory- based meta-analysis of empirical literature. Educational Technology Research and Development, 54(2): 141-176. Schmid, R. F., et al. (2014). The effects of technology use in postsecondary education: A meta-analysis of classroom applications. Computers & Education 72: 271-291.
  • 36. Pierre Dillenbourg (LASI14, Harvard)
  • 37. Scaffolding learning planning Social awareness Recommending competences & resources
  • 38. Knowledge capture is a hard problem
  • 39. Open and ubiquitous user/learner modeling
  • 40. Theory for digital education -revision of Moore’s transactional distances- dialog - structure - autonomy technology
  • 41. Moving from assessment to recognition
  • 42. Moving from assessment to recognition Authenticity of communication, leadership, and information seeking skills