Multi-mediated community structure in a socio-technical network

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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 Learning Analytics and Knowledge 2012 conference

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  • Cite as: Suthers, D. D., & Chu, K.-H. (2012, April 29-May 2, 2012). Multi-mediated community structure in a socio-technical network. Paper presented at Learning Analytics and Knowledge 2012, Vancouver.
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  • Explan title. (shoutout to Devan)
  • I said “Tapped In online community” and …
  • Shout out to Latour
  • (Could replace these two slides with simple explanation of associograms.)
  • Explain the term “Associogram” and its structure in detail. (Could replace these two slides with simple explanation of associograms.)
  • Skipped the Filtered Out -- too much detail
  • Perhaps the detailed numbers are OK here but not needed later.
  • Of course I don’t discuss the detailed numbers but I need to mention the role of R1 at some point.
  • Really don’t need all the numbers!!! Could delete the breakdown of actants . Or leave them there in case needed?
  • Could skip the demo animation; go direct to this. But it’s fun
  • Say something about the significance of the media distribution!
  • Comment on how the partitioning forces nodes into different subgroups even though they have high connectivity. (Stuff at the bottom is only paraphrased briefly.)
  • Did not have time to discuss the actants. Just summarized the third bullet and the results. I did not realize I had two small bullets at the end and talked ahead of them. In the talk I said “within which public communities are embedded” but they aren’t really embedded in THIS network. Maybe put an image from the high degree actant here.
  • Maybe put an image from the high degree actant here.
  • Just to show there were others …
  • Last item is last to transition to next slide, but would otherwise be second to last.
  • Multi-mediated community structure in a socio-technical network

    1. 1. Paper presentation at LearningAnalytics and Knowledge 2012Modified (and backgroundremoved) for SlideShare Multi-mediated Community Structure in a Socio-Technical Network Dan Suthers & Kar-Hai Chu University of Hawaii Supported by the National Science Foundation
    2. 2. Learning in Socio-Technical Networks Learning in university settings, professional communities, and virtual organizations is increasingly technologically embedded – “online,” “distributed,” “networked,” “blended” Fundamental question in all of these settings: how learning and other enhancements of knowledge, skill and capital take place through the interplay between individual and collective agency Demands analyses that connect learning activity in specific times and places with the larger socio- technical network contexts in which they take place
    3. 3. Levels of Learning in STNsHow do social settings foster learning?Agency EpistemologiesWho or what is the agent What is the process of that learns? learning? Individual  Acquisition Small groups  Intersubjective meaning- Networks (communities, making cultures, societies)  Participatory Learners participate in all “levels” simultaneously Need to identify the social settings (“communities” or networks) within which learners participate  Suthers (ijCSCL 2006)
    4. 4. 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 ...
    5. 5. Empirical Community IdentificationSchlager: “I don’t know what communities are there” – Organizational “tenants” and individuals who come for their own enrichment – Multiple forms of participation and mediational means by which participants associate with each other An empirical matter: – Don’t assume that the network constitutes one community – Don’t assume that external communities are replicated within the sociotechnical system Our Approach: – Identify clusters (cohesive subgroups) of participants – Interpret clusters using affiliations and other information – Note media (chats, discussions, files) through which they interact
    6. 6. Relevance of Mediation Multi-mediated: TI and other environments offer multiple means of participation, each with their own interactional and social affordances Choice of technologies by which people keep in touch both reflects and reaffirms the relationship between interlocutors (Licoppe and Smoreda, 2005) Apply this idea to collective rather than dyadic level: communities are embedded within and make use of technological media for interaction in ways that reflect and reaffirm their nature Our approach identifies the mediational means simultaneously with identification of cohesive subgroups (candidate communities of actants)
    7. 7. Traces Analytic Hierarchy Basic needs Activity is distributed across media: – Reunite traces of interaction into a unified analytic artifact Logs may record activity in the wrong ontology: – Abstract event data to other appropriate levels of description (interaction, mediated associations, ties) Sequential interaction analysis and aggregate network analysis are complementary: – 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 analysis  Suthers (HICSS 2011)  Suthers & Rosen (LAK 2011)
    8. 8. Interaction Affiliations Uptake Ties Contingencies Mediated Associations
    9. 9. Portion of an Associogramdiscussions actors files
    10. 10. Tapped In Data Selected 2 year period of high activity Parsed and filtered logs of user activity involving files, asynchronous threaded discussion forums, and quasi- synchronous chat rooms Events: accessing (reading and downloading) or contributing (posting and uploading) to one of these three artifact types Filtered out: – Private chats – Activity in the K-12 (student) campus – Guest accounts – Indirect file access (portrait displays)
    11. 11. Tapped In Associogram 40,490 vertices = actants: – 19,842 actors (49.00%) – 12,037 discussions (29.73%), – 5,862 files (14.48%) – 2,749 chat rooms (6.79%) 229,072 edges = associations 20,431,944 events (sum of weighted degree)
    12. 12. Weighted Degrees = EventsArtifact In-degree = “reads”, Out-degree = “writes”; Reverse for actors Weighted Weighted Totals In-Degree Out-DegreeChat 12,220,792 2,512,887 14,733,679RoomsDiscussions 5,592,946 45,085 5,638,031Files 54,372 5,862 60,234Artifact 17,868,110 2,563,834 20,431,944TotalsActors 2,563,834 17,868,110 20,431,944
    13. 13. Tapped In Associogram 40,490 vertices = actants: – 19,842 actors (49.00%) – 12,037 discussions (29.73%), – 5,862 files (14.48%) – 2,749 chat rooms (6.79%) 229,072 edges = events 20,431,944 events (sum of weighted degree) Average path length: 4.398 – This is bipartite graph: actor-actor path length about half! – Largely due to Tapped In Reception (R1), normalized betweeness centrality 0.665; weighted degree 2,511,057; unweighted degree 18,810 – When R1 removed, average path length = 6.02
    14. 14. Finding Communities in Associogram TI is a network; communities are embedded Community: cohesive subgroup with identifiable common identity, purpose, and/or task “Community detection” (modularity partitioning) algorithm due to Blondel et al. – Maximize intra-partition connectivity in relation to inter- partition connectivity (NP Hard) Computed and visualized in Gephi (gephi.org): – beta OSS for network analysis and visualization – handles large graphs Examined properties (e.g., organizational affiliation) of high degree nodes in each partition to interpret as communities
    15. 15. Visualization of Partitions in OpenOrd 171 Partitions Modularity: 0.817  Blondel, V. D., Guillaume, J.- L., Lambiotte, R., & Lefebvre, E. (2008). Fast unfolding of communities in large networks. Journal of Statistical Mechanics: Theory and Experiment, http://dx.doi.org/10.1088/1742-5 468/2008/10/P10008. Martin, 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).
    16. 16. Largest Six PartitionsP1 8452 actants P2 5826 actants (20.87%): 6953 (14.39%): 2485 actors, 673 chat actors, 782 chat rooms, 495 rooms, 1828 discussions, and discussions, and 331 files 731 files. 29698 edges 20459 edges (12.96%) (8.93%)P3, P4 2565 actants P5, P6 1251 actants (6.33%): 851 (3.09%): 112 actors, 103 chat actors, 35 chat rooms, 1286 rooms, 1006 discussions, 325 discussions, 98 files. files. 1630 actants 1037 actants (4.03%): 857 (2.56%): 729 actors, 26 chat actors, 153 chat rooms, 605 rooms, 71 discussions, 142 discussions, 220 files files
    17. 17. Interpreting P3 2565 actants (6.33%) P3 (red-brown) – 851 actors – 103 chat rooms – 1286 discussions – 325 files Examine high degree actants …
    18. 18. Interpreting P3 2565 actants (6.33%) P3 (red-brown) – 851 actors – 103 chat rooms – 1286 discussions – 325 files Examine high degree actants … All media used, with intensive reading of discussions: – Chat: 272,865 in, 226,561 out SRI Colleague: – Discussion: 355,656 in, 5,976 out CoP mentoring – File sharing: 4717 in, 325 out of new teachers in a Midwestern school district
    19. 19. Interpreting Largest ClustersP1 P2 Co-location suggests a strong relationship Issue of the role of the Tapped In Reception
    20. 20. High Degree NodesActants of unweighted degree greater than 282. Vertex size scaled by weighteddegree. Radial layout with a non-overlapping filter
    21. 21. Interpreting P1 Top actants (unweighted) – R1, Tapped In Reception – R4, public room for Tapped In’s After School Online (ASO) events – R10, the Floor Lobby by which one enters rooms on the Tapped In Groups floor Top actants (weighted) – R1, the Tapped In Reception: 12.29% of all chat events in the network – R3, the personal office of Actor F, an educational researcher Not a community with – R4, the ASO Public Room. its own purpose, but Many highest ranked rooms are owned rather a network for by Tapped In and function to welcome Legitimate Peripheral and route newcomers or as venues for public events open to all Participation by which 18% of actors associated only with R1 other networks are approached Overwhelmingly chat based (1M to 1K)
    22. 22. Interpreting P2 Top Two Actants (weighted) – Actor A: volunteer with normal account and no affiliation, is the most active account in the system. – Actor B a volunteer with facilitator status. The real-world actor was given a second account B’ Taken together, the real word actor B/B’ is as active as Actor A. Most highly ranked actors are help desk volunteers Chat-Based After School Online Events Top ranked discussions and (tightly associated with chats are group rooms, all of P1, separated to meet which are used for ASO events requirements of non- A and B regularly facilitate overlapping modularity these events partitioning)
    23. 23. Summary: Community InterpretationsP1 P2 After School LPP via TI Reception and Online other public Events rooms Mixed Media Chat-basedP3, P4 P5, P6 CoP in a Language Midwestern Arts in the US school district; Midwest; Discussion- Pre-service based program in professional Western US development in the Southern US
    24. 24. Myriad of Small Clusters
    25. 25. Summary & Comments Purely structural (graph theoretic) computations identified cohesive subgroups that have interpretations as communities Demonstrates vibrancy of Tapped In as “transcendent community” ( Joseph et al., CSCL 2007) Value to learning analytics: identify social units that are the setting or agent of learning Need to try with algorithm for overlapping cohesive clusters – Not clique percolation ( Palla et al., Nature 2005) – Edge communities promising ( Ahn et al., Nature 2010) Can “dive in” to examine activity of high-degree actors, structure of chat sessions in rooms, etc.
    26. 26. Related Work: Chat AnalysisFind evidence for relatedness (“uptake”) between chat contributionsStructural analysis of the resulting graph“Folding” into social network
    27. 27. Discussion Dan Sutherssuthers@hawaii.edu lilt.ics.hawaii.edu

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