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Tracing Interaction in Distributed Collaborative Learning

  • Suthers D
  • Medina R
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Abstract

In order to understand how entanglements of the activities of multiple individuals in technology-mediated environments result in learning, it is necessary to trace out activity that may be distributed across time, space and media. Multiple analytic challenges are encountered, including the distributed nature of the data, the contingent nature of human behavior, understanding nonverbal behavior, selective attention to large data sets, and multi-scale phenomena. This paper offers approach to analysis that was developed in our laboratory to address some of these challenges. In order to unify multiple data soucres into one anlytic artifact, we found it useful to abstract from media-specific units of analysis (e.g., adjacency pair, reply) and represent our data using 'contingency graphs' that capture the potential ways in which one act can be contingent upon another. Contingency grpahs serve as abstract transcripts that record distributed interaction in one representation. This chapter describes the contingency graph representation, gives an example of its use in analyzing the development of shared representational practices, and discusses further challenges. ...

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Suthers, D., & Medina, R. (2011). Tracing Interaction in Distributed Collaborative Learning. In Analyzing Interactions in CSCL (pp. 341–366). Springer US. https://doi.org/10.1007/978-1-4419-7710-6_16

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