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.
Original language | English |
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Title of host publication | 13th International Conference on Computer Supported Collaborative Learning |
Subtitle of host publication | Conference Proceedings |
Editors | Kristine Lund, Gerald Niccolai, Elise Lavoue, Cindy Hmelo-Silver, Gahgene Gweon, Michael Baker |
Place of Publication | Lyon France |
Publisher | International Society of the Learning Sciences (ISLS) |
Pages | 112-119 |
Number of pages | 8 |
ISBN (Electronic) | 9781732467231 |
Publication status | Published - 2019 |
Externally published | Yes |
Event | Computer Supported Collaborative Learning 2019 - Lyon, France Duration: 17 Jun 2019 → 21 Jun 2019 Conference number: 13th https://www.cscl2019.com/ https://www.isls.org/cscl/2019/www.cscl2019.com/index.html (Proceedings) |
Publication series
Name | Computer-Supported Collaborative Learning Conference, CSCL |
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Publisher | International Society of the Learning Sciences, Inc. |
Volume | 1 |
ISSN (Electronic) | 1573-4552 |
Conference
Conference | Computer Supported Collaborative Learning 2019 |
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Abbreviated title | CSCL 2019 |
Country/Territory | France |
City | Lyon |
Period | 17/06/19 → 21/06/19 |
Internet address |