Article
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Anti-cheating Online Exams by Minimizing the Cheating Gain
Version 1
: Received: 13 May 2020 / Approved: 31 May 2020 / Online: 31 May 2020 (20:20:46 CEST)
How to cite: Li, M.; Sikdar, S.; Xia, L.; Wang, G. Anti-cheating Online Exams by Minimizing the Cheating Gain. Preprints 2020, 2020050502. https://doi.org/10.20944/preprints202005.0502.v1 Li, M.; Sikdar, S.; Xia, L.; Wang, G. Anti-cheating Online Exams by Minimizing the Cheating Gain. Preprints 2020, 2020050502. https://doi.org/10.20944/preprints202005.0502.v1
Abstract
Cheating prevention in online exams is often hard and costly to tackle with proctoring, and it even sometimes involves privacy issues, especially in social distancing due to the pandemic of COVID-19. Here we propose a low-cost and privacy-preserving anti-cheating scheme by programmatically minimizing the cheating gain. A novel anti-cheating scheme we developed theoretically ensures that the cheating gain of all students can be controlled below a desired level aided by the prior knowledge of students’ abilities and a proper assignment of question sequences. Furthermore, a heuristic greedy algorithm we developed can refine an assignment of questions from a cyclic pool of question sequences to efficiently reduce the cheating gain. Compared to the integer linear programming and min-max matching methods in a small-scale simulation, our heuristic algorithm provides results close to the optimal solutions offered by the two standard discrete optimization methods. Hence, our anti-cheating approach could potentially be a cost-effective solution to the well-known cheating problem even without proctoring.
Keywords
Online exam, cheating prevention, discrete optimization, social distancing
Subject
Computer Science and Mathematics, Computer Science
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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