Towards a Social Learning Analytics for Online Communities of Practice for Educators


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Presentation on social learning analytics for online professional learning by Kathleen Perez-Lopez and I at Learning Analytics and Knowledge, May 2, 2012 in Vancouver.

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  • Add AIR & ED? logos, new colors and fonts
  • Difficult to see many patterns beyond the fact that participation is highly skewed. There are a few dense dark edges in the upper portion of the network, between relatively few members and topics. In fact, a few forums received most of the posts; a few members initiated most of the topics and made most of the posts, and a few topics received half of the posts). The 59 large heavy-posting members – those with 20 up to 582 posts – are allotted almost two-thirds of the vertical space. If members had been spread uniformly, the heavily skewed activity would have been even more apparent.However, we can see that the distribution of edges is not smooth from the upper portions of the network to the lower, and there appear to be a number of concentrations in mid-figure. There might be some interesting activity by members in those regions, but with this static view, it is difficult to see what that could be.
  • 1- post nodes removed: topics with only one post made to them; members who posted only onceEdge color (black to yellow) and opacity are logarithmically proportional to the number of times a member posted to the topic
  • Looks Like Healthy Evolving CommunityIt might show how time bounded activity targeted at some subgroup could be leveraged into more sustained and general engagement.
  • Goal = no member islandsGo from bi-model to member-member and topic-topic Look for repartition where topics are maximally distinct and people are maximally connected Add cite
  • Topic network of 556 nodes is very dense: 0.48; average degree is 267. Doesn’t seem to decompose to many separable subnetworks. Group
  • Unique identifiers for respondents index to SNA/content analysis data
  • Towards a Social Learning Analytics for Online Communities of Practice for Educators

    1. 1. First Steps Towards a Social Learning Analytics for Online Communities of Practice for Educators Darren Cambridge Kathleen Perez-Lopez
    2. 2. Community Cultivation • Now – – • Coming soon – EPIC-ed Dropout prevention and recovery
    3. 3. Outreach • Connect & Inspire • Briefs • Community directory • Innovations blog • Connected Educators Month
    4. 4. ResearchEvolution Tracking evolution of five emerging online communities; examining critical decisions made by leaders and the ways in which decisions are informed by data, resources, and people.Value creation Collecting value creation stories and survey data from a range of established communities to determine which online activities, content, and interactive features best support learning and provide value to educators.Engagement Beginning design-based research in new EPIC-Ed community. Current focus is on design interventions to increase “connectedness” among educators.Social roles Exploring the use of social network analysis in four communities to identify and better understand the connecting patterns and social roles of online community leaders.
    5. 5. Research Team• Researchers • Case study partners – Darren Cambridge, AIR – Al Byers, NSTA – Kathleen Perez-Lopez, – Sheryl Nussbaum- AIR Beach, PLP – Rachel Crossno, AIR – Sharon Roth, NCTE – Sherry Booth, NCSU – Lia Dossin & Geoff – Shaun Kellogg, NCSU Fletcher, SETDA – Bobby Hopgood & Lisa Hervey, NCSU – Jim Burke, English Companion Ning – Andrew Gardner, BrainPop
    6. 6. Learning Analytics Goals• Small set of visualization methods and tools simple enough for regular, direct analysis by community managers• Practitioner question driven• Support reflective dialog about what to do next• More efficient use of expert community moderator judgment• Actionable intelligence  Actuated intelligence
    7. 7. Social Learning Analytics Approaches• Focus on three of Ferguson and Buckingham Shum’s five:• Social learning network analysis• Social learning content analysis• Social learning context analysis
    8. 8. NSTA Learning Center • 8,300+ PD Resources and Opportunities • 100K+ users • Badges and leaderboards • Learning plans and portfolios • Expert advisors • Forums
    9. 9. Learning Needs of Science Teachers• Science teachers need to learn continuously and broadly – To address mandates to teach “out of field” (particularly grades 6-8) – To address topic focus of coming standards that cross disciplines – To incorporate changing body of pedagogical content knowledge• Teachers often come to the Learning Center initially to address an immediate challenge – I need to teach students the difference between weather and climate tomorrow morning• What activities lead to broad and sustained engagement?• How can we lower barriers to entry in conversation while maintaining connections between people?
    10. 10. Year of NSTA LC Posts 9/24/2010 - 9/28/20116978 posts21 forums492 members557 topicsSNA using NodeXL
    11. 11. Quintile 1 9/24/2010 to 1/9/2011Early Months:Very little activity fromthese members
    12. 12. Quintile 2 1/10/2011 to 2/26/20112nd Quintile:Activity building here,but still light
    13. 13. Quintile 3 2/27/2011 to 5/7/20113rd Quintile:Lots of posts toone private forum
    14. 14. Quintile 4 5/8/2011 to 7/25/20114th Quintile:Private forumdied out, but muchmore activity from thesemembers
    15. 15. Quintile 5 7/26/2011 to 9/28/20115th Quintile:Activity concentratedamong these members,and healthy activity amonglower posters.
    16. 16. Repartitioning TopicsFind Fn , a partition of topics, that yields: 1. VERY segregated Topic network, Tn 474 x 281 474 x 474 281 x 474 X Member-Topic Topic-Member Tn 2. UN-segregated member network, Mn 281 x 20+ 281 x 281 20+ x 281 X Fn-Member Mn Member-Fn
    17. 17. Clustering Algorithms• Clauset-Newman-Moore groups (NodeXL)• Wakita-Tsurumi groups (NodeXL)• M-slices and k-cores (Pajek)• Wakita-Tsurumi on a reduced dataset• Wakita-Tsurumi on member network Perez-Lopez, Cambridge, Byers, & Booth (2012) Sunbelt XXXII
    18. 18. Adding Content Analysis• Better to have a different way to represent the natural clustering of topics than by those who post to them – Textual content analysis to locate concepts: LSA + ?• Filtering out non-contextual content – Friendly banter – Useful for other purposes, but interference here
    19. 19. Adding Context Analysis • Pre-hypothesis narrative research using CognitiveEdge SenseMaker Suite • Narrative fragments + quantitative classification by author • “Filter questions” indexed to Wenger, Trayner, & DeLaat’s (2011) five cycles of value creation • Authors linked to usage data
    20. 20. Adding Cases• Powerful Learning Practice• TFANet• Classroom 2.0• Intel Teachers Engage• Individual ego-centric cross-network maps – E.g., NSTA + PLP + Facebook + Twitter
    21. 21. Key Questions We’re Thinking About• Significant differences in purpose, context, and theories of learning – Are the managers questions likely to be similar enough? – Is there likely to be a set of visualizations that can be useful across contexts?• Can techniques of sufficient power to tell managers something they don’t already know be made sufficiently accessible that they actually use them?• Which techniques are most likely to be worth focusing on next?
    22. 22. We’d Love to Hear From You• @edcocp• Darren Cambridge +1-202-270-5224 @dcambrid