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A framework for conceptualizing, representing, and analyzing distributed interaction

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Abstract

The relationship between interaction and learning is a central concern of the learning sciences, and analysis of interaction has emerged as a major theme within the current literature on computer-supported collaborative learning. The nature of technology-mediated interaction poses analytic challenges. Interaction may be distributed across actors, space, and time, and vary from synchronous, quasi-synchronous, and asynchronous, even within one data set. Often multiple media are involved and the data comes in a variety of formats. As a consequence, there are multiple analytic artifacts to inspect and the interaction may not be apparent upon inspection, being distributed across these artifacts. To address these problems as they were encountered in several studies in our own laboratory, we developed a framework for conceptualizing and representing distributed interaction. The framework assumes an analytic concern with uncovering or characterizing the organization of interaction in sequential records of events. The framework includes a media independent characterization of the most fundamental unit of interaction, which we call uptake. Uptake is present when a participant takes aspects of prior events as having relevance for ongoing activity. Uptake can be refined into interactional relationships of argumentation, information sharing, transactivity, and so forth for specific analytic objectives. Faced with the myriad of ways in which uptake can manifest in practice, we represent data using graphs of relationships between events that capture the potential ways in which one act can be contingent upon another. These contingency graphs serve as abstract transcripts that document in one representation interaction that is distributed across multiple media. This paper summarizes the requirements that motivate the framework, and discusses the theoretical foundations on which it is based. It then presents the framework and its application in detail, with examples from our work to illustrate how we have used it to support both ideographic and nomothetic research, using qualitative and quantitative methods. The paper concludes with a discussion of the framework’s potential role in supporting dialogue between various analytic concerns and methods represented in CSCL.

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Notes

  1. The mapping of temporal proximity to evidential strength is relative to the medium and activity. Here, a person is deliberating over various materials while her partner works asynchronously. A few minutes deliberation is plausible.

  2. It may seem impossible for an object created at 1:45:49 to become available at 1:45:33. We remind the reader that the computer clocks were not synchronized. The analogy of a time zone may be useful. In real time, 1:45:33 in P2’s “time zone” is after 1:45:49 in P1’s “time zone.” It would have been easy to hide this from readers by changing the time stamps in the figure. However, we decided to leave the discrepancy in to emphasize the point that even if the clocks were synchronized it would be misleading to compare times across the upper and lower half of the figure due to the asynchronous updating, and more importantly, that the contingency graph can handle partially specified orderings of events from distinct timelines.

  3. In disCourse, a list of who has read each message at what time is available to participants on demand in a separate display, but this analysis suggests that other awareness visualizations may be useful, such as summaries of activity prior to a posting.

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Acknowledgments

The studies and analyses on which this paper is based were supported by the National Science Foundation under award 0093505. The work developed during years of intensive discussion among the authors that also benefited from interaction with numerous colleagues in our laboratory and elsewhere. We especially thank Gerry Stahl for his ongoing commentary on these ideas and the anonymous reviewers for comments that helped address problems with prior drafts.

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  1. Laboratory for Interactive Learning Technologies, Department of Information and Computer Sciences, University of Hawai‘i at Manoa, 1680 East West Road, POST 309, Honolulu, HI, 96822, USA

    Daniel D. Suthers, Nathan Dwyer & Richard Medina

  2. Center for Applied ICT, Copenhagen Business School, Howitzvej 60, 2.floor, Frederiksberg, DK-2000, Denmark

    Ravi Vatrapu

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  1. Daniel D. Suthers
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Correspondence to Daniel D. Suthers.

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Suthers, D.D., Dwyer, N., Medina, R. et al. A framework for conceptualizing, representing, and analyzing distributed interaction. Computer Supported Learning 5, 5–42 (2010). https://doi.org/10.1007/s11412-009-9081-9

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