Examining motivations and self-regulated learning strategies of returning MOOCs learners

Bodong Chen, Yizhou Fan, Guogang Zhang, Qiong Wang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Scopus citations

Abstract

The present study examines behavioral patterns, motivations, and self-regulated learning strategies of returning learners-a special learner subpopulation in massive open online courses (MOOCs). To this end, data were collected from a teacher professional development MOOC that has been offered for seven iterations during 2014-2016. Data analysis identified more than 15% of all registrants as returning learners. Findings from click log analysis identified possible motivations of re-enrollment including improving grades, refreshing theoretical understanding, and solving practical problems. Further analysis uncovered evidence of self-regulated learning strategies among returning learners. Taken together, this study contributes to ongoing inquiry into MOOCs learning pathways, informs future MOOC design, and sheds light on the exploration of MOOCs as a viable option for teacher professional development.

Original languageEnglish (US)
Title of host publicationLAK 2017 Conference Proceedings - 7th International Learning Analytics and Knowledge Conference
Subtitle of host publicationUnderstanding, Informing and Improving Learning with Data
PublisherAssociation for Computing Machinery
Pages542-543
Number of pages2
ISBN (Electronic)9781450348706
DOIs
StatePublished - Mar 13 2017
Event7th International Conference on Learning Analytics and Knowledge, LAK 2017 - Vancouver, Canada
Duration: Mar 13 2017Mar 17 2017

Publication series

NameACM International Conference Proceeding Series

Other

Other7th International Conference on Learning Analytics and Knowledge, LAK 2017
CountryCanada
CityVancouver
Period3/13/173/17/17

Bibliographical note

Publisher Copyright:
© 2017 ACM.

Keywords

  • Clustering
  • MOOCs
  • Teacher professional development

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