Machine learning based approach to exam cheating detection

Kamalov, Firuz
Sulieman, Hana
Calonge, David Santandreu
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Public Library of Science
The 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.
This article is not available at CUD collection. The version of scholarly record of this article is published in PLoS ONE (2021), available online at:
administrative personnel, algorithm, article, human, human experiment, outlier detection, recurrent neural network
Kamalov, 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.0254340