Integrating Learning Analytics to Measure Message Quality in Large Online Conversations

dc.contributor.author Eryilmaz, Evren
dc.contributor.author Thoms, Brian
dc.contributor.author Ahmed, Zafor
dc.contributor.author Sandhu, Avneet
dc.date.accessioned 2020-01-04T07:08:33Z
dc.date.available 2020-01-04T07:08:33Z
dc.date.issued 2020-01-07
dc.description.abstract Research on computer-supported collaborative learning (CSCL) often employs content analysis as an approach to investigate message quality in asynchronous online discussions using systematic message-coding schemas. Although this approach helps researchers count the frequencies by which students engage in different socio-cognitive actions, it does not explain how students articulate their ideas in categorized messages. This study investigates the effects of a recommender system on the quality of students’ messages from voluminous discussions. We employ learning analytics to produce a quasi-quality index score for each message. Moreover, we examine the relationship between this score and the phases of a popular message-coding schema. Empirical findings show that a custom CSCL environment extended by a recommender system supports students to explore different viewpoints and modify interpretations with higher quasi-quality index scores than students assigned to the control software. Theoretical and practical implications are also discussed.
dc.format.extent 10 pages
dc.identifier.doi 10.24251/HICSS.2020.007
dc.identifier.isbn 978-0-9981331-3-3
dc.identifier.uri http://hdl.handle.net/10125/63746
dc.language.iso eng
dc.relation.ispartof Proceedings of the 53rd Hawaii International Conference on System Sciences
dc.rights Attribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.uri https://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subject Advances in Teaching and Learning Technologies
dc.subject computer-supported collaborative learning
dc.subject content analysis
dc.subject learning analytics
dc.subject message quality
dc.subject recommender system
dc.title Integrating Learning Analytics to Measure Message Quality in Large Online Conversations
dc.type Conference Paper
dc.type.dcmi Text
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