Machine learning based approach to exam cheating detection

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dc.contributor.authorKamalov, Firuz
dc.contributor.authorSulieman, Hana
dc.contributor.authorCalonge, David Santandreu
dc.date.accessioned2021-08-16T11:32:28Z
dc.date.available2021-08-16T11:32:28Z
dc.date.copyright© 2021
dc.date.issued2021-08
dc.descriptionThis article is not available at CUD collection. The version of scholarly record of this article is published in PLoS ONE (2021), available online at: https://doi.org/10.1371/journal.pone.0254340en_US
dc.description.abstractThe COVID-19 pandemic has impelled the majority of schools and universities around the world to switch to remote teaching. One of the greatest challenges in online education is preserving the academic integrity of student assessments. The lack of direct supervision by instructors during final examinations poses a significant risk of academic misconduct. In this paper, we propose a new approach to detecting potential cases of cheating on the final exam using machine learning techniques. We treat the issue of identifying the potential cases of cheating as an outlier detection problem. We use students’ continuous assessment results to identify abnormal scores on the final exam. However, unlike a standard outlier detection task in machine learning, the student assessment data requires us to consider its sequential nature. We address this issue by applying recurrent neural networks together with anomaly detection algorithms. Numerical experiments on a range of datasets show that the proposed method achieves a remarkably high level of accuracy in detecting cases of cheating on the exam. We believe that the proposed method would be an effective tool for academics and administrators interested in preserving the academic integrity of course assessments. Copyright: © 2021 Kamalov et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.en_US
dc.identifier.citationKamalov, F., Sulieman, H., & Calonge, D. S. (2021). Machine learning based approach to exam cheating detection. PLoS ONE, 16(8 August) doi:10.1371/journal.pone.0254340en_US
dc.identifier.issn19326203
dc.identifier.urihttps://doi.org/10.1371/journal.pone.0254340
dc.identifier.urihttp://hdl.handle.net/20.500.12519/426
dc.language.isoenen_US
dc.publisherPublic Library of Scienceen_US
dc.relationAuthors Affiliations : Kamalov, F., Department of Electrical Engineering, Canadian University Dubai, Dubai, United Arab Emirates; Sulieman, H., Department of Mathematics and Statistics, American University of Sharjah, Sharjah, United Arab Emirates; Calonge, D.S., Department of Communication and Media, Canadian University Dubai, Dubai, United Arab Emirates
dc.relation.ispartofseriesPLoS ONE;Volume 16, Issue 8 August
dc.rightsCreative Commons Attribution 4.0 International (CC BY 4.0) License
dc.rights.holderCopyright: © 2021 Kamalov et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectadministrative personnelen_US
dc.subjectalgorithmen_US
dc.subjectarticleen_US
dc.subjecthumanen_US
dc.subjecthuman experimenten_US
dc.subjectoutlier detectionen_US
dc.subjectrecurrent neural networken_US
dc.titleMachine learning based approach to exam cheating detectionen_US
dc.typeArticleen_US

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