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dc.contributor.authorSeaton, Daniel
dc.contributor.authorBergner, Yoav
dc.contributor.authorChuang, Isaac
dc.contributor.authorMitros, Piotr
dc.contributor.authorPritchard, David E
dc.date.accessioned2017-07-21T15:37:51Z
dc.date.available2017-07-21T15:37:51Z
dc.date.issued2014-04
dc.identifier.issn0001-0782
dc.identifier.urihttp://hdl.handle.net/1721.1/110801
dc.description.abstractMassive open online courses (MOOCs) collect valuable data onstudent learning behavior: essentially complete records of all student interactions in a self-contained learning environment,with the benefit of large sample sizes. We present an overview of how the 108,000 participants behaved in 6.002x - Circuits and Electronics, the first course in MITx (now edX). We divide participants into tranches based on the extent of their assessment activities, ranging from browsers (who constituted ~ 76% of the participants but accounted for only 8% of the total time spent in the course) to certificate-earners (7% of theparticipants who accounted for 60% of the total time). We examine how the certificate earners allocated their time amongst the various course components and study what fraction of each they accessed. We analyze transitions between course components, showing, how student behavior differs when solving homework vs. exam problems. This work lays the foundation for future studies of how use of various course components, and transitions among them, influence learning in MOOCs.en_US
dc.description.sponsorshipNational Science Foundation (U.S.) (DUE-1044294)en_US
dc.language.isoen_US
dc.publisherAssociation for Computing Machinery (ACM)en_US
dc.relation.isversionofhttp://dx.doi.org/10.1145/2500876en_US
dc.rightsCreative Commons Attribution-Noncommercial-Share Alikeen_US
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/4.0/en_US
dc.sourceOther repositoryen_US
dc.titleWho does what in a massive open online course?en_US
dc.typeArticleen_US
dc.identifier.citationSeaton, Daniel T.; Bergner, Yoav; Chuang, Isaac et al. “Who Does What in a Massive Open Online Course?” Communications of the ACM 57, 4 (April 2014): 58–65en_US
dc.contributor.departmentMassachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratoryen_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Scienceen_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Physicsen_US
dc.contributor.departmentMassachusetts Institute of Technology. Office of Digital Learningen_US
dc.contributor.departmentMassachusetts Institute of Technology. Research Laboratory of Electronicsen_US
dc.contributor.mitauthorSeaton, Daniel
dc.contributor.mitauthorBergner, Yoav
dc.contributor.mitauthorChuang, Isaac
dc.contributor.mitauthorMitros, Piotr
dc.contributor.mitauthorPritchard, David E
dc.relation.journalCommunications of the ACMen_US
dc.eprint.versionOriginal manuscripten_US
dc.type.urihttp://purl.org/eprint/type/JournalArticleen_US
eprint.statushttp://purl.org/eprint/status/NonPeerRevieweden_US
dspace.orderedauthorsSeaton, Daniel T.; Bergner, Yoav; Chuang, Isaac; Mitros, Piotr; Pritchard, David E.en_US
dspace.embargo.termsNen_US
dc.identifier.orcidhttps://orcid.org/0000-0001-7296-523X
dc.identifier.orcidhttps://orcid.org/0000-0001-5697-1496
mit.licenseOPEN_ACCESS_POLICYen_US
mit.metadata.statusComplete


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