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Conference Paper: A systematic review of studies on predicting student learning outcomes using learning analytics

TitleA systematic review of studies on predicting student learning outcomes using learning analytics
Authors
Issue Date2017
PublisherAssociation for Computing Machinery (ACM).
Citation
Proceedings of the 7th International Learning Analytics and Knowledge (LAK '17) Conference, Simon Fraser University, Vancouver, Canada, 13-17 March 2017, p. 528-529 How to Cite?
AbstractPredicting student learning outcomes is one of the prominent themes in Learning Analytics research. These studies varied to a significant extent in terms of the techniques being used, the contexts in which they were situated, and the consequent effectiveness of the prediction. This paper presented the preliminary results of a systematic review of studies in predictive learning analytics. With the goal to find out what methodologies work for what circumstances, this study will be able to facilitate future research in this area, contributing to relevant system developments that are of pedagogic values.
Persistent Identifierhttp://hdl.handle.net/10722/243496
ISBN

 

DC FieldValueLanguage
dc.contributor.authorHu, X-
dc.contributor.authorCheong, CWL-
dc.contributor.authorDing, W-
dc.contributor.authorWoo, M-
dc.date.accessioned2017-08-25T02:55:36Z-
dc.date.available2017-08-25T02:55:36Z-
dc.date.issued2017-
dc.identifier.citationProceedings of the 7th International Learning Analytics and Knowledge (LAK '17) Conference, Simon Fraser University, Vancouver, Canada, 13-17 March 2017, p. 528-529-
dc.identifier.isbn9781450348706-
dc.identifier.urihttp://hdl.handle.net/10722/243496-
dc.description.abstractPredicting student learning outcomes is one of the prominent themes in Learning Analytics research. These studies varied to a significant extent in terms of the techniques being used, the contexts in which they were situated, and the consequent effectiveness of the prediction. This paper presented the preliminary results of a systematic review of studies in predictive learning analytics. With the goal to find out what methodologies work for what circumstances, this study will be able to facilitate future research in this area, contributing to relevant system developments that are of pedagogic values.-
dc.languageeng-
dc.publisherAssociation for Computing Machinery (ACM).-
dc.relation.ispartofProceedings of the Seventh International Learning Analytics & Knowledge Conference (LAK '17)-
dc.titleA systematic review of studies on predicting student learning outcomes using learning analytics-
dc.typeConference_Paper-
dc.identifier.emailHu, X: xiaoxhu@hku.hk-
dc.identifier.authorityHu, X=rp01711-
dc.identifier.doi10.1145/3027385.3029438-
dc.identifier.hkuros275097-
dc.identifier.spage528-
dc.identifier.epage529-
dc.publisher.placeNew York, NY-

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