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Article: What predicts student satisfaction with MOOCs: A gradient boosting trees supervised machine learning and sentiment analysis approach

TitleWhat predicts student satisfaction with MOOCs: A gradient boosting trees supervised machine learning and sentiment analysis approach
Authors
Issue Date2020
Citation
Computers & Education, 2020, v. 145, p. 1-16 How to Cite?
Persistent Identifierhttp://hdl.handle.net/10722/291188

 

DC FieldValueLanguage
dc.contributor.authorHew, KFT-
dc.contributor.authorHu, X-
dc.contributor.authorQiao, C-
dc.contributor.authorTang, Y-
dc.date.accessioned2020-11-07T13:53:29Z-
dc.date.available2020-11-07T13:53:29Z-
dc.date.issued2020-
dc.identifier.citationComputers & Education, 2020, v. 145, p. 1-16-
dc.identifier.urihttp://hdl.handle.net/10722/291188-
dc.languageeng-
dc.relation.ispartofComputers & Education-
dc.titleWhat predicts student satisfaction with MOOCs: A gradient boosting trees supervised machine learning and sentiment analysis approach-
dc.typeArticle-
dc.identifier.emailHew, KFT: kfhew@hku.hk-
dc.identifier.authorityHew, KFT=rp01873-
dc.identifier.doi10.1016/j.compedu.2019.103724-
dc.identifier.scopuseid_2-s2.0-85072959228-
dc.identifier.hkuros318620-
dc.identifier.volume145-
dc.identifier.spage1-
dc.identifier.epage16-

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