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Title: Finding Common Ground: A Method for Measuring Recent Temporal Context in Analyses of Complex, Collaborative Thinking
Authors: Ruis, Andrew
Siebert-Evenstone, Amanda
Pozen, Rebecca
Eagan, Brendan
Shaffer, D.W.
Issue Date: Jun-2019
Publisher: International Society of the Learning Sciences (ISLS)
Citation: Ruis, A., Siebert-Evenstone, A., Pozen, R., Eagan, B., & Shaffer, D. (2019). Finding Common Ground: A Method for Measuring Recent Temporal Context in Analyses of Complex, Collaborative Thinking. In Lund, K., Niccolai, G. P., Lavoué, E., Hmelo-Silver, C., Gweon, G., & Baker, M. (Eds.), A Wide Lens: Combining Embodied, Enactive, Extended, and Embedded Learning in Collaborative Settings, 13th International Conference on Computer Supported Collaborative Learning (CSCL) 2019, Volume 1 (pp. 136-143). Lyon, France: International Society of the Learning Sciences.
Abstract: Complex, collaborative thinking is often conceptualized as a process of developing cognitive connections among the contributions of different participants. A central problem in modeling collaboration in this way is thus determining, for any contribution to a discussion, the appropriate context for modeling the connections being made--that is, for determining the appropriate recent temporal context. Recent temporal context is typically defined using a moving window of fixed length. However, that length is dependent on the setting, and there are no existing methods for reliably determining an appropriate window length. This paper presents an empirical method for measuring recent temporal context, and thus for defining an appropriate window length to be used in analyses of complex, collaborative thinking. Importantly, the method we describe minimizes the need for human annotation while providing both qualitative and quantitative warrants for choosing a particular window length.
Appears in Collections:CSCL 2019

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