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A Framework for Multi-Level Analysis of Distributed Interaction
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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 ...

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 A Framework for Multi-Level Analysis of Distributed Interaction Presentation Transcript

    • 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
    • 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
    • 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 ….
    • 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)
    • Origins in a detailed manual analysis of multimodal collaborationSuthers Dwyer, Medina & Vatrapu, ijCSCL 2010 Nathan Dwyer Richard Medina Ravi Vatrapu
    • TracesAnalyticHierarchy Suthers, HICSS 2011; Suthers & Rosen, LAK2011
    • (this portion of presentation is an Omnigraffle animation stepping through the Traces analytic hierarchy as it was explained verbally …)
    • ExamplesAnalyses of Tapped In Chat structure Technology embedded “communities” Relationships (time permitting)
    • 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 ...
    • Exposing ChatFeatures ThroughAnalysis ofUptake BetweenContributionsDan SuthersCaterina DesiatoHICSS 2012Kar-Hai ChuNathan Dwyer
    • 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?
    • 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?
    • 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
    • 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
    • Contribution Uptake and Sociogram• Structural correlations about 0.5 for uptake graph, but 0.9 for proximity prestige• Led to rule improvement
    • 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.
    • (Here showed software process in other tools …)
    • 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)
    • Contingency Graph, 24 Hours in ASO • Recoloring for modularity classes (cohesive subgroups) • Clearly shows phases of interaction
    • Folded Sociogram Nodes are actorsNode size is page rank. Colors are modularity classes
    • One Month of Activity, ASO Room
    • Multi-MediatedCommunityStructure in aSocio-TechnicalNetworkDan SuthersKar-Hai ChuLAK 2012(first talk onMonday!)
    • 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
    • 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 …
    • 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).
    • 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.
    • High Degree Nodes
    • 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 …
    • (a brief demonstration of interpreting modularity classes)
    • Myriad of Small Groups
    • Exploring howTechnologyMediatesRelationships inSocio-TechnicalSystemsKar-Hai Chu Dissertation
    • 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
    • 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
    • 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
    • (slides from dissertation inprogress removed pending publication)
    • 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?
    • Discussion Dan Sutherssuthers@hawaii.edu lilt.ics.hawaii.edu