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Who's Cheating? Mining Patterns of Collusion from Text and Events in Online Exams

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Bibliographic Details
Title: Who's Cheating? Mining Patterns of Collusion from Text and Events in Online Exams
Language: English
Authors: Cleophas, Catherine (ORCID 0000-0002-3676-2782), Hönnige, Christoph, Meisel, Frank, Meyer, Philipp
Source: INFORMS Transactions on Education. Jan 2023 23(2):84-94.
Availability: Institute for Operations Research and the Management Sciences (INFORMS). 5521 Research Park Drive Suite 200, Catonsville, Maryland 21228. Tel: 800-446-3676; Tel: 443-757-3500; Fax: 443-757-3515; e-mail: informs@informs.org; Web site: https://pubsonline.informs.org/journal/ited
Peer Reviewed: Y
Page Count: 11
Publication Date: 2023
Document Type: Journal Articles
Reports - Descriptive
Education Level: Higher Education
Postsecondary Education
Descriptors: Computer Assisted Testing, Cheating, Identification, Essay Tests, Learning Analytics, Test Construction, College Students
DOI: 10.1287/ited.2021.0260
ISSN: 1532-0545
Abstract: As the COVID-19 pandemic motivated a shift to virtual teaching, exams have increasingly moved online too. Detecting cheating through collusion is not easy when tech-savvy students take online exams at home and on their own devices. Such online at-home exams may tempt students to collude and share materials and answers. However, online exams' digital output also enables computer-aided detection of collusion patterns. This paper presents two simple data-driven techniques to analyze exam event logs and essay-form answers. Based on examples from exams in social sciences, we show that such analyses can reveal patterns of student collusion. We suggest using these patterns to quantify the degree of collusion. Finally, we summarize a set of lessons learned about designing and analyzing online exams.
Abstractor: As Provided
Entry Date: 2023
Accession Number: EJ1372401
Database: ERIC
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Abstract:As the COVID-19 pandemic motivated a shift to virtual teaching, exams have increasingly moved online too. Detecting cheating through collusion is not easy when tech-savvy students take online exams at home and on their own devices. Such online at-home exams may tempt students to collude and share materials and answers. However, online exams' digital output also enables computer-aided detection of collusion patterns. This paper presents two simple data-driven techniques to analyze exam event logs and essay-form answers. Based on examples from exams in social sciences, we show that such analyses can reveal patterns of student collusion. We suggest using these patterns to quantify the degree of collusion. Finally, we summarize a set of lessons learned about designing and analyzing online exams.
ISSN:1532-0545
DOI:10.1287/ited.2021.0260