Using Machine Learning to Detect 'Multiple-Account'Cheating and Analyze the Influence of Student and Problem Features

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dc.contributor.author Ruipérez-Valiente, José A.
dc.contributor.author Muñoz Merino, Pedro José
dc.contributor.author Alexandron, Giora
dc.contributor.author Pritchard, Dave
dc.date.accessioned 2021-01-12T16:13:09Z
dc.date.available 2021-01-12T16:13:09Z
dc.date.issued 2017-12-18
dc.identifier.bibliographicCitation Ruipérez-Valiente, J. A., Muñoz-Merino, P. J., Alexandron, G., and Pritchard, D. E. Using Machine Learning to Detect ‘Multiple- Account’ Cheating and Analyze the Influence of Student and Problem Features. IEEE Transactions on Learning Technologies, vol. 12, no. 1, pp. 112-122, 1 Jan-March 2019
dc.identifier.issn 1939-1382
dc.identifier.uri http://hdl.handle.net/10016/31685
dc.description.abstract One of the reported methods of cheating in online environments in the literature is CAMEO (Copying Answers using Multiple Existences Online), where harvesting accounts are used to obtain correct answers that are later submitted in the master account which gives the student credit to obtain a certificate. In previous research, we developed an algorithm to identify and label submissions that were cheated using the CAMEO method; this algorithm relied on the IP of the submissions. In this study, we use this tagged sample of submissions to i) compare the influence of student and problems characteristics on CAMEO and ii) build a random forest classifier that detects submissions as CAMEO without relying on IP, achieving sensitivity and specificity levels of 0.966 and 0.996, respectively. Finally, we analyze the importance of the different features of the model finding that student features are the most important variables towards the correct classification of CAMEO submissions, concluding also that student features have more influence on CAMEO than problem features.
dc.description.sponsorship The first and second authors want to thank the Madrid Regional Government with grant No. S2013/ICE-2715, the Spanish Ministry of Economy and Competitiveness projects RESET (TIN2014-53199-C3-1-R), the European Erasmus+ projects MOOC Maker (561533-EPP-1-2015-1-ES-EPPKA2- CBHE-JP) and SHEILA (562080-EPP-1-2015-BE-EPPKA3-PIFORWARD) for partially supporting this work. The authors would like to thank Zhongzhou Chen and Christopher Chudzicki for their help conducting our original research about CAMEO
dc.description.sponsorship The first and second authors want to thank the Madrid Regional Government with grant No. S2013/ICE-2715, the Spanish Ministry of Economy and Competitiveness projects RESET (TIN2014-53199-C3-1-R), the European Erasmus+ projects MOOC Maker (561533-EPP-1-2015-1-ES-EPPKA2- CBHE-JP) and SHEILA (562080-EPP-1-2015-BE-EPPKA3-PIFORWARD) for partially supporting this work. The authors would like to thank Zhongzhou Chen and Christopher Chudzicki for their help conducting our original research about CAMEO
dc.language.iso eng
dc.publisher IEEE
dc.rights © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
dc.subject.other Academic dishonesty
dc.subject.other Educational data mining
dc.subject.other Machine learning
dc.subject.other MOOCS
dc.subject.other Academic dishonesty
dc.title Using Machine Learning to Detect 'Multiple-Account'Cheating and Analyze the Influence of Student and Problem Features
dc.type article
dc.subject.eciencia Telecomunicaciones
dc.identifier.doi https://dx.doi.org/10.1109/TLT.2017.2784420
dc.rights.accessRights openAccess
dc.relation.projectID Gobierno de España. TIN2014-53199-C3-1-R
dc.relation.projectID Comunidad de Madrid. S2013/ICE-2715
dc.relation.projectID Gobierno de España. TIN2014-53199-C3-1-R
dc.type.version acceptedVersion
dc.identifier.publicationfirstpage 112
dc.identifier.publicationissue 1
dc.identifier.publicationlastpage 122
dc.identifier.publicationtitle IEEE Transactions on Learning Technologies
dc.identifier.publicationvolume 12
dc.identifier.uxxi AR/0000022300
dc.contributor.funder Ministerio de Economía y Competitividad (España)
dc.contributor.funder Comunidad de Madrid
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