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Optimized collusion prevention for online exams during social distancing

Online education is important in the COVID-19 pandemic, but online exam at individual homes invites students to cheat in various ways, especially collusion. While physical proctoring is impossible during social distancing, online proctoring is costly, compromises privacy, and can lead to prevailing...

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Autores principales: Li, Mengzhou, Luo, Lei, Sikdar, Sujoy, Nizam, Navid Ibtehaj, Gao, Shan, Shan, Hongming, Kruger, Melanie, Kruger, Uwe, Mohamed, Hisham, Xia, Lirong, Wang, Ge
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7921656/
https://www.ncbi.nlm.nih.gov/pubmed/33649355
http://dx.doi.org/10.1038/s41539-020-00083-3
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author Li, Mengzhou
Luo, Lei
Sikdar, Sujoy
Nizam, Navid Ibtehaj
Gao, Shan
Shan, Hongming
Kruger, Melanie
Kruger, Uwe
Mohamed, Hisham
Xia, Lirong
Wang, Ge
author_facet Li, Mengzhou
Luo, Lei
Sikdar, Sujoy
Nizam, Navid Ibtehaj
Gao, Shan
Shan, Hongming
Kruger, Melanie
Kruger, Uwe
Mohamed, Hisham
Xia, Lirong
Wang, Ge
author_sort Li, Mengzhou
collection PubMed
description Online education is important in the COVID-19 pandemic, but online exam at individual homes invites students to cheat in various ways, especially collusion. While physical proctoring is impossible during social distancing, online proctoring is costly, compromises privacy, and can lead to prevailing collusion. Here we develop an optimization-based anti-collusion approach for distanced online testing (DOT) by minimizing the collusion gain, which can be coupled with other techniques for cheating prevention. With prior knowledge of student competences, our DOT technology optimizes sequences of questions and assigns them to students in synchronized time slots, reducing the collusion gain by 2–3 orders of magnitude relative to the conventional exam in which students receive their common questions simultaneously. Our DOT theory allows control of the collusion gain to a sufficiently low level. Our recent final exam in the DOT format has been successful, as evidenced by statistical tests and a post-exam survey.
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spelling pubmed-79216562021-03-12 Optimized collusion prevention for online exams during social distancing Li, Mengzhou Luo, Lei Sikdar, Sujoy Nizam, Navid Ibtehaj Gao, Shan Shan, Hongming Kruger, Melanie Kruger, Uwe Mohamed, Hisham Xia, Lirong Wang, Ge NPJ Sci Learn Article Online education is important in the COVID-19 pandemic, but online exam at individual homes invites students to cheat in various ways, especially collusion. While physical proctoring is impossible during social distancing, online proctoring is costly, compromises privacy, and can lead to prevailing collusion. Here we develop an optimization-based anti-collusion approach for distanced online testing (DOT) by minimizing the collusion gain, which can be coupled with other techniques for cheating prevention. With prior knowledge of student competences, our DOT technology optimizes sequences of questions and assigns them to students in synchronized time slots, reducing the collusion gain by 2–3 orders of magnitude relative to the conventional exam in which students receive their common questions simultaneously. Our DOT theory allows control of the collusion gain to a sufficiently low level. Our recent final exam in the DOT format has been successful, as evidenced by statistical tests and a post-exam survey. Nature Publishing Group UK 2021-03-01 /pmc/articles/PMC7921656/ /pubmed/33649355 http://dx.doi.org/10.1038/s41539-020-00083-3 Text en © The Author(s) 2021 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
spellingShingle Article
Li, Mengzhou
Luo, Lei
Sikdar, Sujoy
Nizam, Navid Ibtehaj
Gao, Shan
Shan, Hongming
Kruger, Melanie
Kruger, Uwe
Mohamed, Hisham
Xia, Lirong
Wang, Ge
Optimized collusion prevention for online exams during social distancing
title Optimized collusion prevention for online exams during social distancing
title_full Optimized collusion prevention for online exams during social distancing
title_fullStr Optimized collusion prevention for online exams during social distancing
title_full_unstemmed Optimized collusion prevention for online exams during social distancing
title_short Optimized collusion prevention for online exams during social distancing
title_sort optimized collusion prevention for online exams during social distancing
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7921656/
https://www.ncbi.nlm.nih.gov/pubmed/33649355
http://dx.doi.org/10.1038/s41539-020-00083-3
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