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Context Aware CSCL: Moving Toward Contextualized Analysis

Context Aware CSCL: Moving Toward Contextualized Analysis. CSCL 2011 Sean P. Goggins , PhD James Laffey , PhD Chris Amelung , PhD. Standing on the Shoulders of Giants. Dan Suthers (Dependency Graphs) Peter Reimann (Stochastic Process Models)

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Context Aware CSCL: Moving Toward Contextualized Analysis

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  1. Context Aware CSCL: Moving Toward Contextualized Analysis CSCL 2011 Sean P. Goggins, PhD James Laffey, PhD Chris Amelung, PhD

  2. Standing on the Shoulders of Giants • Dan Suthers (Dependency Graphs) • Peter Reimann (Stochastic Process Models) • Gerry Stahl (Ethnomethodologically Informed) • Chris Teplovs • Carolyn Rose • Gregory Dyke • Rebecca Reynolds

  3. Context of Research • Learning – Online Graduate Level Classes • Software engineering • Mylyn • Bugzilla • Disaster relief • Online political discourse in Facebook • Virtual Math Teams • Medical software user communities • Wikis for Game Design • Adult Kickball league • Online dating

  4. Underpants Gnomes With much discourtesy from South Park

  5. Underpants Gnomes

  6. CANS is Different • We designed the trace data collection to incorporate context

  7. Context Aware Logging Read Read course Person Module Post Group DB Person Post Page

  8. Contextualized Analysis • This paper describes how we use mixed methods to inform log analysis in CANS. • Our study of multiple systems is informing design of future logging infrastructures

  9. Goggins, Laffey & GalyenACM Group Proceedings, 2010 • How do different activity types shape the social organization and practices of completely online CSCL Groups? • How bi-directional (as opposed to uni-directional) activity logs make group interactions and activity types more visible.

  10. What Did We Find? (That we couldn’t find with context aware logging like CANS) • Bi-Directional Data Matters: If we just look at post data, we don’t see the richness of the network. Variance in Density, Centralization, Betweenness & other key social network measures do not discriminate until we add read data. • Activity Type: We do see connections between the activity types and the network statistics and structures • New Meanings for Old Statistics (Your Data Really Does Matter): For example, the difference between lurkers and influencers/connectors: High Betweenness Centrality has two semantics in online log analysis.

  11. Aggregate Lesson Based On: • 3 Years • 670,000 Posts • 14 Papers published; 7 using CANS data; All integrating qualitative analysis and network analysis iteratively • 7 Papers under review with various data sets using these methods • 10 distinct data sets; 16 specific corpora

  12. Unique Perspective • A broad knowledge of knowledge construction, information behavior, identity development, work production, coordination, discourse & mating behaviors, and how they can be understood through analysis of electronic trace data. • Patterns: • Within Contexts • Between Contexts

  13. Integrating Log Data

  14. Where does Weight Come from?

  15. Where Does Weight Come From? • Interviews • Virtual Ethnography • Content analysis • Qualitative coding • Surveys of self reported social networks

  16. Take Away • Future: Models that can be applied to new data sets to support real time visualizations • Aggregation informed by mixed methods analysis enables the construction of models durable across different processes (creating a high level structure) • Each Context is different, but looking across contexts and using supporting data to inform log analysis provides a path for both context specific and general understanding of the models that are useful for visualization of Learning, coordination, information behavior, identity, discourse, leadership and group emergence.

  17. Sean Goggins, Jim Laffey & Chris Amelung Thank You

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