Activating Latent Knowledge
Dragan Gasevic
George Siemens
edX
July 3, 2014
Agenda
1. Latent Capacity
2. Legacy assumptions
3. cMOOCs
4. Future directions (lessons)
Google’s technology infrastructure
Occupy Wall Street
Arab Spring
What are the tools?
100 people in a room theory of
knowledge
The power of integration…
Education is waiting for its latency
activating tools
What is required
Making transparent what we know (declaring
knowledge, explicit or mined)
Creating a persistence and progr...
Agenda
1. Latent Capacity
2. Legacy assumptions
3. cMOOCs
4. Future directions (lessons)
Legacy traces:
Assumptions that need to change
Grading
Teaching practices
Education theories
Networked models of learning
Agenda
1. Latent Capacity
2. Legacy assumptions
3. cMOOCs
4. Future directions (lessons)
Who/what influences
information flows in a cMOOC?
What/who drives
community formation in a cMOOC?
CCK11 – student demographics
Socio-technical approach to
network analysis
Most active participants
Node W1 W5 W6 W12 Description Domain
@cck11feeds 0 282 447 1160 Course Aggregator N/A
@web20educa...
Most influential nodes
Distribution of weighted input degree for weeks 1, 5, 6, and 12, for the top 10
ranked nodes within...
Network authorities
Variation of the authority weights for the top ranked social and technological nodes, over
the 12 week...
Network authorities
Variation of the authority weights for the top ranked social nodes, over the 12 weeks of
the course
Network hubs
Variation of the hub weights for the top ranked nodes, over the 12 weeks of the course
Network brokers
Variation of the betweenness centrality values for the top ranked nodes, over the 12
weeks of the course
Network centers
Variation of the input closeness centrality values for the top ranked nodes, over the 12
weeks of the cour...
Community formation
Network modularity
19 communities identified
Newman, M. E. (2006). Modularity and community structure ...
Community formation
26%
Community formation
26% 25%
Community formation
12%
Community formation
12% 9%
Agenda
1. Latent Capacity
2. Legacy assumptions
3. cMOOCs
4. Future directions (lessons)
PKG
Capturing, mining, inferring what a learner
knows
CB
1. PKG
2. Granularization of learning content.
3. Match PKG with knowledge of a field.
4. ??
5. Fill gaps
6. Get recogn...
Implications on/of
technology design
Pedagogy vs. technology
Pedagogy vs. technology
Lou, Y., et al. (2006). Media and pedagogy in undergraduate distance education: A theory-
based me...
Pierre Dillenbourg (LASI14, Harvard)
Scaffolding learning planning
Social awareness
Recommending competences &
resources
Knowledge capture is
a hard problem
Open and ubiquitous
user/learner modeling
Theory for digital education
-revision of Moore’s transactional distances-
dialog - structure - autonomy
technology
Moving
from assessment to
recognition
Moving
from assessment to
recognition
Authenticity of communication, leadership,
and information seeking skills
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    1. 1. Activating Latent Knowledge Dragan Gasevic George Siemens edX July 3, 2014
    2. 2. Agenda 1. Latent Capacity 2. Legacy assumptions 3. cMOOCs 4. Future directions (lessons)
    3. 3. Google’s technology infrastructure
    4. 4. Occupy Wall Street
    5. 5. Arab Spring
    6. 6. What are the tools?
    7. 7. 100 people in a room theory of knowledge
    8. 8. The power of integration…
    9. 9. Education is waiting for its latency activating tools
    10. 10. What is required Making transparent what we know (declaring knowledge, explicit or mined) Creating a persistence and progressive identity (knowledge map)
    11. 11. Agenda 1. Latent Capacity 2. Legacy assumptions 3. cMOOCs 4. Future directions (lessons)
    12. 12. Legacy traces: Assumptions that need to change Grading Teaching practices Education theories Networked models of learning
    13. 13. Agenda 1. Latent Capacity 2. Legacy assumptions 3. cMOOCs 4. Future directions (lessons)
    14. 14. Who/what influences information flows in a cMOOC?
    15. 15. What/who drives community formation in a cMOOC?
    16. 16. CCK11 – student demographics
    17. 17. Socio-technical approach to network analysis
    18. 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. 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. 20. Network authorities Variation of the authority weights for the top ranked social and technological nodes, over the 12 weeks of the course
    21. 21. Network authorities Variation of the authority weights for the top ranked social nodes, over the 12 weeks of the course
    22. 22. Network hubs Variation of the hub weights for the top ranked nodes, over the 12 weeks of the course
    23. 23. Network brokers Variation of the betweenness centrality values for the top ranked nodes, over the 12 weeks of the course
    24. 24. Network centers Variation of the input closeness centrality values for the top ranked nodes, over the 12 weeks of the course
    25. 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. 26. Community formation 26%
    27. 27. Community formation 26% 25%
    28. 28. Community formation 12%
    29. 29. Community formation 12% 9%
    30. 30. Agenda 1. Latent Capacity 2. Legacy assumptions 3. cMOOCs 4. Future directions (lessons)
    31. 31. PKG Capturing, mining, inferring what a learner knows
    32. 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. 33. Implications on/of technology design
    34. 34. Pedagogy vs. technology
    35. 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. 36. Pierre Dillenbourg (LASI14, Harvard)
    37. 37. Scaffolding learning planning Social awareness Recommending competences & resources
    38. 38. Knowledge capture is a hard problem
    39. 39. Open and ubiquitous user/learner modeling
    40. 40. Theory for digital education -revision of Moore’s transactional distances- dialog - structure - autonomy technology
    41. 41. Moving from assessment to recognition
    42. 42. Moving from assessment to recognition Authenticity of communication, leadership, and information seeking skills
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