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dc.contributor.authorChen, Chen
dc.contributor.authorSonnert, Gerhard
dc.contributor.authorSadler, Philip
dc.contributor.authorMalan, David
dc.date.accessioned2020-05-11T10:43:39Z
dc.date.issued2020-02-27
dc.identifier.citationChen, Chen, Gerhard Sonnert, Philip M. Sadler, and David J. Malan. 2020. Computational Thinking and Assignment Resubmission Predict Persistence in a Computer Science MOOC. Journal of Computer Assisted Learning: 1–14.en_US
dc.identifier.issn0266-4909en_US
dc.identifier.issn1365-2729en_US
dc.identifier.urihttp://nrs.harvard.edu/urn-3:HUL.InstRepos:42665380*
dc.description.abstractMassive open online course (MOOC) studies have shown that precourse skills (such as precomputational thinking) and course engagement measures (such as making multiple submission attempts with assignments when the initial submission is incorrect) predict students' grade performance, yet little is known about whether these factors predict students' course retention. In applying survival analysis to a sample of more than 20,000 participants from one popular computer science MOOC, we found that students' precomputational thinking skills and their perseverance in assignment submission strongly predict their persistence in the MOOC. Moreover, we discovered that precomputational thinking skills, programming experience, and gender, which were previously considered to be constant predictors of students' retention, have effects that attenuate over the course milestones. This finding suggests that MOOC educators should take a growth perspective towards students' persistence: As students overcome the initial hurdles, their resilience grows stronger.en_US
dc.description.sponsorshipAstronomyen_US
dc.description.sponsorshipOther Research Uniten_US
dc.language.isoen_USen_US
dc.publisherWileyen_US
dash.licenseMETA_ONLY
dc.subjectEducationen_US
dc.subjectComputer Science Applicationsen_US
dc.titleComputational Thinking and Assignment Resubmission Predict Persistence in a Computer Science MOOCen_US
dc.typeJournal Articleen_US
dc.description.versionVersion of Recorden_US
dc.relation.journalJournal of Computer Assisted Learningen_US
dash.depositing.authorMalan, David
dc.date.available2020-05-11T10:43:39Z
dash.affiliation.otherFaculty of Arts & Sciencesen_US
dash.funder.nameNational Science Foundationen_US
dash.funder.award1352696en_US
dc.identifier.doi10.1111/jcal.12427
dc.source.journalJ Comput Assist Learn
dash.contributor.affiliatedSadler, Philip
dash.contributor.affiliatedMalan, David
dash.contributor.affiliatedChen, Chen
dash.contributor.affiliatedSonnert, Gerhard


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