Integrating Learning Analytics to Measure Message Quality in Large Online Conversations
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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