Does order matter? Investigating sequential and cotemporal models of collaboration

Zachari Swiecki, Zheming Lian, Andrew R. Ruis, David Williamson Shaffer

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

4 Citations (Scopus)

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 languageEnglish
Title of host publication13th International Conference on Computer Supported Collaborative Learning
Subtitle of host publicationConference Proceedings
EditorsKristine Lund, Gerald Niccolai, Elise Lavoue, Cindy Hmelo-Silver, Gahgene Gweon, Michael Baker
Place of PublicationLyon France
PublisherInternational Society of the Learning Sciences (ISLS)
Pages112-119
Number of pages8
ISBN (Electronic)9781732467231
Publication statusPublished - 2019
Externally publishedYes
EventComputer Supported Collaborative Learning 2019 - Lyon, France
Duration: 17 Jun 201921 Jun 2019
Conference number: 13th
https://www.cscl2019.com/
https://www.isls.org/cscl/2019/www.cscl2019.com/index.html (Proceedings)

Publication series

NameComputer-Supported Collaborative Learning Conference, CSCL
PublisherInternational Society of the Learning Sciences, Inc.
Volume1
ISSN (Electronic)1573-4552

Conference

ConferenceComputer Supported Collaborative Learning 2019
Abbreviated titleCSCL 2019
CountryFrance
CityLyon
Period17/06/1921/06/19
Internet address

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