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Title: Does Order Matter? Investigating Sequential and Cotemporal Models of Collaboration
Authors: Swiecki, Zachari
Lian, Zheming
Ruis, Andrew
Shaffer, D.W.
Issue Date: Jun-2019
Publisher: International Society of the Learning Sciences (ISLS)
Citation: Swiecki, Z., Lian, Z., Ruis, A., & Shaffer, D. (2019). Does Order Matter? Investigating Sequential and Cotemporal Models of Collaboration. 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. 112-119). Lyon, France: International Society of the Learning Sciences.
Abstract: Many researchers have argued that models of collaborative processes should account for temporality, but there exist different approaches for doing so. We compared two specific approaches to modeling collaborative processes in a CSCL context: Epistemic Network Analysis, which models events cotemporally (unordered and temporally proximate), and Sequential Pattern Mining, which models events sequentially (ordered and temporally proximate). Our results suggest that in this context cotemporal models constructed with Epistemic Network Analysis outperform sequential models constructed with Sequential Pattern Mining in terms of (a) explanatory power, (b) efficiency, and (c) interpretability.
Appears in Collections:CSCL 2019

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