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 language||English (US)|
|Title of host publication||LAK 2017 Conference Proceedings - 7th International Learning Analytics and Knowledge Conference|
|Subtitle of host publication||Understanding, Informing and Improving Learning with Data|
|Publisher||Association for Computing Machinery|
|Number of pages||2|
|State||Published - Mar 13 2017|
|Event||7th International Conference on Learning Analytics and Knowledge, LAK 2017 - Vancouver, Canada|
Duration: Mar 13 2017 → Mar 17 2017
|Name||ACM International Conference Proceeding Series|
|Other||7th International Conference on Learning Analytics and Knowledge, LAK 2017|
|Period||3/13/17 → 3/17/17|
Bibliographical notePublisher Copyright:
© 2017 ACM.
- Teacher professional development