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Integrating Learning Analytics to Measure Message Quality in Large Online Conversations

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Title:Integrating Learning Analytics to Measure Message Quality in Large Online Conversations
Authors:Eryilmaz, Evren
Thoms, Brian
Ahmed, Zafor
Sandhu, Avneet
Keywords:Advances in Teaching and Learning Technologies
computer-supported collaborative learning
content analysis
learning analytics
message quality
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Date Issued:07 Jan 2020
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.
Pages/Duration:10 pages
URI:http://hdl.handle.net/10125/63746
ISBN:978-0-9981331-3-3
DOI:10.24251/HICSS.2020.007
Rights:Attribution-NonCommercial-NoDerivatives 4.0 International
https://creativecommons.org/licenses/by-nc-nd/4.0/
Appears in Collections: Advances in Teaching and Learning Technologies


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