File Download

There are no files associated with this item.

  Links for fulltext
     (May Require Subscription)
Supplementary

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
KeywordsMoocs
Machine learning
Sentiment analysis
Learner satisfaction
Online learning
Issue Date2020
PublisherPergamon. The Journal's web site is located at http://www.elsevier.com/locate/compedu
Citation
Computers & Education, 2020, v. 145, p. article no. 103724 How to Cite?
AbstractThis study defines MOOC success as the extent of student satisfaction with the course. Having more satisfied MOOC students can extend the reach of an institution to more people, build the brand name of the institution, and even help the institution use MOOCs as a source of revenue. Traditionally, student completion rate is frequently used to define MOOC success, which however, is often inaccurate because many students have no intention of finishing a MOOC. Informed by Moore's theory of transactional distance, this study adopted supervised machine learning algorithm, sentiment analysis and hierarchical linear modelling to analyze the course features of 249 randomly sampled MOOCs and 6393 students' perceptions of these MOOCs. The results showed that course instructor, content, assessment, and schedule play significant roles in explaining student satisfaction, while course structure, major, duration, video, interaction, perceived course workload and perceived difficulty play no significant roles. This study adds to the extant literature by examining specific learner-level and course-level factors that can predict MOOC learner satisfaction and estimating their relative effects. Implications for MOOC instructors and practitioners are also provided.
Persistent Identifierhttp://hdl.handle.net/10722/291188
ISSN
2020 Impact Factor: 8.538
2015 SCImago Journal Rankings: 3.143
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorHew, KF-
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. article no. 103724-
dc.identifier.issn0360-1315-
dc.identifier.urihttp://hdl.handle.net/10722/291188-
dc.description.abstractThis study defines MOOC success as the extent of student satisfaction with the course. Having more satisfied MOOC students can extend the reach of an institution to more people, build the brand name of the institution, and even help the institution use MOOCs as a source of revenue. Traditionally, student completion rate is frequently used to define MOOC success, which however, is often inaccurate because many students have no intention of finishing a MOOC. Informed by Moore's theory of transactional distance, this study adopted supervised machine learning algorithm, sentiment analysis and hierarchical linear modelling to analyze the course features of 249 randomly sampled MOOCs and 6393 students' perceptions of these MOOCs. The results showed that course instructor, content, assessment, and schedule play significant roles in explaining student satisfaction, while course structure, major, duration, video, interaction, perceived course workload and perceived difficulty play no significant roles. This study adds to the extant literature by examining specific learner-level and course-level factors that can predict MOOC learner satisfaction and estimating their relative effects. Implications for MOOC instructors and practitioners are also provided.-
dc.languageeng-
dc.publisherPergamon. The Journal's web site is located at http://www.elsevier.com/locate/compedu-
dc.relation.ispartofComputers & Education-
dc.subjectMoocs-
dc.subjectMachine learning-
dc.subjectSentiment analysis-
dc.subjectLearner satisfaction-
dc.subjectOnline learning-
dc.titleWhat predicts student satisfaction with MOOCs: A gradient boosting trees supervised machine learning and sentiment analysis approach-
dc.typeArticle-
dc.identifier.emailHew, KF: kfhew@hku.hk-
dc.identifier.authorityHew, KF=rp01873-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1016/j.compedu.2019.103724-
dc.identifier.scopuseid_2-s2.0-85072959228-
dc.identifier.hkuros318620-
dc.identifier.volume145-
dc.identifier.spagearticle no. 103724-
dc.identifier.epagearticle no. 103724-
dc.identifier.isiWOS:000505184000020-
dc.publisher.placeUnited Kingdom-
dc.identifier.issnl0360-1315-

Export via OAI-PMH Interface in XML Formats


OR


Export to Other Non-XML Formats