A Framework for Multi-Level Analysis of Distributed Interaction


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Interaction, Mediation, and Ties: A Framework for Multi-Level Analysis of Distributed Interaction (presented at the workshop on Connecting Levels and Methods of Analysis in Networked Communities at the Learning Analytics and Knowledge Conference 2012, Vancouver)

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  • If you need to cite this work, for the analytic hierarchy cite: Suthers, D. D., & Rosen, D. (2011). A unified framework for multi-level analysis of distributed learning Proceedings of the First International Conference on Learning Analytics & Knowledge, Banff, Alberta, February 27-March 1, 2011.
    For the chat analysis: Suthers, D. D., & Desiato, C. (2012). Exposing chat features through analysis of uptake between contributions. Proceedings of the Hawaii International Conference on the System Sciences (HICSS-45), January 4-7, 2012, Grand Wailea, Maui, Hawai‘i (CD-ROM). New Brunswick: Institute of Electrical and Electronics Engineers, Inc. (IEEE).
    For community structure (this is also a talk in Slideshare): Suthers, D. D., & Chu, K.-H. (2012, April 29-May 2, 2012). Multi-mediated community structure in a socio-technical network. Paper presented at the To be presented in Learning Analytics and Knowledge 2012
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A Framework for Multi-Level Analysis of Distributed Interaction

  1. 1. Presentation at the Workshop on Connecting Levels and Methods of Analysis in NetworkedInteraction, Communities at the Learning Analytics and Knowledge Conference 2012, VancouverMediation, and (Version edited for Slideshare)Ties A Framework for Multi-Level Analysis of Distributed Interaction Dan Suthers University of Hawaii Supported by the National Science Foundation
  2. 2. Preview Motivations – Analytic Challenges of Technologically-embedded interaction – Phenomena at simultaneous granularities (individual, small group, network) interact A framework for representing data at multiple levels in a connected way – Maps from events to contingencies, uptake, associations, ties Examples of analyses at different levels – Automating contingency/uptake analysis of chats – “Community detection” by finding cohesive mediated subgroups
  3. 3. Traces Analytic Hierarchy (“Traces” is our NSF-funded project) Basic needs – Reunite traces of interaction into a unified analytic artifact – Abstract event data to other appropriate levels of description (interaction, mediated associations, ties) – Enable mapping between these descriptions both ways The Traces analytic hierarchy addresses these issues  Abstract transcript representation that collects relevant events into a single analytic artifact  Analytic hierarchy that supports multiple levels of analysisLet’s look at the concepts, then the representations ….
  4. 4. Concepts Contingencies: (Suthers Dwyer, Medina & Vatrapu, ijCSCL 2010) – Manifest relationships between acts and their setting (including other events) – Includes media structures (e.g., “reply-to”), temporal and spatial proximity, lexical overlap, semantic overlap … – Evidence for Uptake (what we really care about) Uptake: (Suthers, ijCSCL 2006) – Taking some aspect of (the trace of) a prior act or event as relevant for ongoing activity – A generalized unit of analysis for “interaction” broadly understood (multi/cross-media; inter/intra-subjective) Mediation and Associations – All interaction is mediated; actors are associated via media – We want to understand how social phenomena are technologically embedded ( Licoppe & Smoreda, Social Networks 2005)
  5. 5. Origins in a detailed manual analysis of multimodal collaborationSuthers Dwyer, Medina & Vatrapu, ijCSCL 2010 Nathan Dwyer Richard Medina Ravi Vatrapu
  6. 6. TracesAnalyticHierarchy Suthers, HICSS 2011; Suthers & Rosen, LAK2011
  7. 7. (this portion of presentation is an Omnigraffle animation stepping through the Traces analytic hierarchy as it was explained verbally …)
  8. 8. ExamplesAnalyses of Tapped In Chat structure Technology embedded “communities” Relationships (time permitting)
  9. 9. Tapped InSRI’s Network of education professionals: PD and peer support (Mark Schlager, Patti Schank, Judi Fusco)Since 1997: longest running educational online community 8 years of data (7.4G) 20K educators/year 800 user spaces QuickTimeª and a decompressor are needed to see this picture. 50 tenants 40-60 volunteer-run community-wide activities per month Chats, threaded discussions, wikis, resource sharing ...
  10. 10. Exposing ChatFeatures ThroughAnalysis ofUptake BetweenContributionsDan SuthersCaterina DesiatoHICSS 2012Kar-Hai ChuNathan Dwyer
  11. 11. MotivationsEmbedding of learning and work in socio-technical networks leads to questions such as: Where are the most engaged discussions? Who are the central actors in these discussions, in terms of promoting discussion by others? What ideas receive the most development? How does the interplay between individual and collective agency lead to desirable outcomes?
  12. 12. Sequential AnalysisSequential structure of interaction is relevant Engagement is displayed when actors take up each other’s contributions. Central actors can be identified by how their contributions are taken up by others. Identification of the development of ideas requires tracing out threads of discussionHuman analysis is slow: can we automate The installation of contingencies Their combination into uptake… sufficiently well to find useful structure?
  13. 13. Formative Case AnalysisFirst we did a manual study to compare human analysis to rule-based (automatable) analysis, in order to improve the latter After School Online Session on mentoring in the schools with genuine engagement by participants in addressing professional issues Human interpretative analysis of uptake Rule-based installation of contingencies (temporal, actor, address & reply, lexical), combined into uptake
  14. 14. Example Transcript Portion184 23:35 Maria: are all good teachers good mentors?185 23:38 Andrea: some people will take a while to get to that point186 23:42 Andrea: No..not all187 23:51 Nancy: definitely not188 23:55 Helen: Training can help, but I think some is personality189 24:09 Ashley: some people are excellent teachers but are horrible mentors190 24:09 Nancy: some great teachers can not hold a decent conversation with an adult191 24:11 Andrea: i had to co-ops who would be awful mentors192 24:24 Helen: Nods193 24:27 Lisa: That is an interesting question Maria, ... I would probably say yes first off, and then wonder some more194 24:42 Maria: it is something I have thought about often Lisa195 24:47 Andrea: I think its alot of personality196 25:17 Lisa: one thing a mentor has to know is how to operate with a peer, and ow to be intentional about handing over, or encouraging greater independence197 25:18 Maria: observation has made me think that it takes an extra “special ingredient” to tip the scales198 25:34 Nancy: I think if you have the passion for teaching you will want everyone else to feel the same199 25:35 Andrea: agree
  15. 15. Contribution Uptake and Sociogram• Structural correlations about 0.5 for uptake graph, but 0.9 for proximity prestige• Led to rule improvement
  16. 16. Automating Contingency AnalysisWork in progress (demonstration available)Example following: 24 hours in ASOBastian, M., Heymann, S., & Jacomy, M. (2009). Gephi: An open source software for exploring andmanipulating networks. International AAAI Conference on Weblogs and Social Media.
  17. 17. (Here showed software process in other tools …)
  18. 18. Contingency Graph, 24 Hours in ASO • Colors are Actors • Nodes are chat contributions (size is weighted in-degree) • Links are weighted by contingencies (evidence for uptake)
  19. 19. Contingency Graph, 24 Hours in ASO • Recoloring for modularity classes (cohesive subgroups) • Clearly shows phases of interaction
  20. 20. Folded Sociogram Nodes are actorsNode size is page rank. Colors are modularity classes
  21. 21. One Month of Activity, ASO Room
  22. 22. Multi-MediatedCommunityStructure in aSocio-TechnicalNetworkDan SuthersKar-Hai ChuLAK 2012(first talk onMonday!)
  23. 23. Finding Communities in Associogram TI is a network; communities are embedded Associogram of Actors and Artifacts (Chats, Discussions, Files): ~40K nodes, 229K edges Gephi.org: – beta OSS for network analysis and visualization – handles large graphs “Community detection” (modularity partitioning) algorithm due to Blondel et al. Examine properties (e.g., organizational affiliation) of high degree nodes in each partition to interpret as communities
  24. 24. Visualization: Fruchterman-Reingold Choosing the right algorithm … A classic force-directed QuickTimeª and a layout algorithm … run decompressor are needed to see this picture. for 48 hours on a quad core machine …
  25. 25. Better Visualization: OpenOrdMartin, S., Brown, W. M., Klavans, R., & Boyack, K. (2011). OpenOrd: An Open-Source Toolbox forLarge Graph Layout. Paper presented at the SPIE Conference on Visualization and Data Analysis(VDA).
  26. 26. Top 6 Cohesive SubgroupsBlondel, V. D., Guillaume, J.-L., Lambiotte, R., & Lefebvre, E. (2008). Fast unfolding of communities inlarge networks. Journal of Statistical Mechanics: Theory and Experiment,http://dx.doi.org/10.1088/1742-5468/2008/10/P10008.
  27. 27. High Degree Nodes
  28. 28. Community Interpretations Associations After School via TI Online Reception and Events other public rooms CoP in a Chat-based Midwestern Language school district; Arts in the US Discussion- Midwest; based Pre-service professional program in development Western US in the Southern US Let’s look at this in Gephi …
  29. 29. (a brief demonstration of interpreting modularity classes)
  30. 30. Myriad of Small Groups
  31. 31. Exploring howTechnologyMediatesRelationships inSocio-TechnicalSystemsKar-Hai Chu Dissertation
  32. 32. Overview Motivation: Selection and timing of media reflects and reaffirms status of interpersonal relationships (Licoppe & Smoreda, 2005) RQ: How does technology mediate relationships that are formed in sociotechnical systems? – Describe mediated interactions in terms of how they are embedded in the technology
  33. 33. Method Interactions mediated by 3 artifact types – Files – Discussions – Chats Find associogram and create vector for each pair Perform cluster analysis on the vectors to find ‘types’ of relationships – Stepwise, iterating for hierarchical breakdown
  34. 34. Interpretations of Clusters 2.2 = good friends, balanced relationship (high volume) 1 97.6% 2.4% 2 2.1 = long-term peers/colleagues (high volume) 1.2 = short-term peers/colleagues 89.2% 10.8% 95.4% 4.6% – Leader/followers exist here 1.1 1.2 2.1 2.2 1.1.3 = acquaintances – Leader/followers exist here 1.1.2/1.1.1 = very low 77.1% 13.9% 9.0% frequency of interaction 1.1. 1.1. 1.1. (no relationship) 1 2 3
  35. 35. (slides from dissertation inprogress removed pending publication)
  36. 36. Tying together the levelsExample scenario Compute contingencies --> sociogram on all scheduled chat sessions Identify sessions with desired structural characteristics (e.g., high participation, role balanced) Microanalysis of selected sessions Identify persons playing roles (via both microanalysis and sociograms) in learning-relevant events Are these global roles? How did they come into the roles? What communities do they participate in? Via what media do the relevant interactions take place?
  37. 37. Discussion Dan Sutherssuthers@hawaii.edu lilt.ics.hawaii.edu