A framework for conceptualizing, representing, and analyzing distributed interaction

  • Daniel D. Suthers
  • Nathan Dwyer
  • Richard Medina
  • Ravi Vatrapu


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.


Theoretical and methodological framework Interaction analysis Distributed learning Uptake Contingency graphs 



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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Copyright information

© International Society of the Learning Sciences, Inc.; Springer Science + Business Media, LLC 2010

Authors and Affiliations

  • Daniel D. Suthers
    • 1
  • Nathan Dwyer
    • 1
  • Richard Medina
    • 1
  • Ravi Vatrapu
    • 2
  1. 1.Laboratory for Interactive Learning TechnologiesDepartment of Information and Computer Sciences, University of Hawai‘i at ManoaHonoluluUSA
  2. 2.Center for Applied ICTCopenhagen Business SchoolFrederiksbergDenmark

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