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dc.contributor.authorAlexandron, Giora
dc.contributor.authorRuipérez-Valiente, José A. 
dc.contributor.authorChen, Zhongzhou
dc.contributor.authorMuñoz-Merino, Pedro J.
dc.contributor.authorPritchard, David E.
dc.date.accessioned2021-07-13T09:28:45Z
dc.date.available2021-07-13T09:28:45Z
dc.date.issued2017-05
dc.identifier.issn0360-1315
dc.identifier.urihttp://hdl.handle.net/20.500.12761/339
dc.description.abstractThis paper presents a detailed study of a form of academic dishonesty that involves the use of multiple accounts for harvesting solutions in a Massive Open Online Course (MOOC). It is termed CAMEO – Copying Answers using Multiple Existence Online. A person using CAMEO sets up one or more harvesting accounts for collecting correct answers; these are then submitted in the user's master account for credit. The study has three main goals: Determining the prevalence of CAMEO, studying its detailed characteristics, and inferring the motivation(s) for using it. For the physics course that we studied, about 10% of the certificate earners used this method to obtain more than 1% of their correct answers, and more than 3% of the certificate earners used it to obtain the majority (>50%) of their correct answers. We discuss two of the likely consequences of CAMEO: jeopardizing the value of MOOC certificates as academic credentials, and generating misleading conclusions in educational research. Based on our study, we suggest methods for reducing CAMEO. Although this study was conducted on a MOOC, CAMEO can be used in any learning environment that enables students to have multiple accounts.
dc.language.isoeng
dc.publisherElsevier
dc.titleCopying@Scale: Using Harvesting Accounts for Collecting Correct Answers in a MOOCen
dc.typejournal article
dc.journal.titleComputers & Education
dc.type.hasVersionVoR
dc.rights.accessRightsopen access
dc.volume.number108
dc.identifier.doihttp://dx.doi.org/10.1016/j.compedu.2017.01.015
dc.page.final114
dc.page.initial96
dc.subject.keywordAcademic dishonesty
dc.subject.keywordEducational data mining
dc.subject.keywordLearning analytics
dc.subject.keywordMOOCs
dc.description.statuspub
dc.eprint.idhttp://eprints.networks.imdea.org/id/eprint/1544


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