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Mike Perkins; Jasper Roe; Darius Postma; James McGaughran; Don Hickerson – Journal of Academic Ethics, 2024
This study explores the capability of academic staff assisted by the Turnitin Artificial Intelligence (AI) detection tool to identify the use of AI-generated content in university assessments. 22 different experimental submissions were produced using Open AI's ChatGPT tool, with prompting techniques used to reduce the likelihood of AI detectors…
Descriptors: Artificial Intelligence, Student Evaluation, Identification, Natural Language Processing
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Yang Zhen; Xiaoyan Zhu – Educational and Psychological Measurement, 2024
The pervasive issue of cheating in educational tests has emerged as a paramount concern within the realm of education, prompting scholars to explore diverse methodologies for identifying potential transgressors. While machine learning models have been extensively investigated for this purpose, the untapped potential of TabNet, an intricate deep…
Descriptors: Artificial Intelligence, Models, Cheating, Identification
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Lim, Kieran Fergus – Physics Education, 2022
Undergraduate first-year courses are often mandatory for students in a variety of majors and degrees. Many students view these core courses as of little interest and relevance, which is associated with lack of motivation for study and can lead to cheating. Contract cheating in text-based is difficult to detect and prove. Contract cheating in…
Descriptors: College Freshmen, Contracts, Cheating, Assignments
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Harper, Rowena; Bretag, Tracey; Rundle, Kiata – Higher Education Research and Development, 2021
This article contributes to an emerging body of research on the role of assessment design in the prevention and detection of contract cheating. Drawing on the largest contract cheating dataset gathered to date (see cheatingandassessment.edu.au), this article examines the types of assignments and exams in which students self-reported having engaged…
Descriptors: Cheating, Identification, College Students, College Faculty
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Mike Richards; Kevin Waugh; Mark A Slaymaker; Marian Petre; John Woodthorpe; Daniel Gooch – ACM Transactions on Computing Education, 2024
Cheating has been a long-standing issue in university assessments. However, the release of ChatGPT and other free-to-use generative AI tools has provided a new and distinct method for cheating. Students can run many assessment questions through the tool and generate a superficially compelling answer, which may or may not be accurate. We ran a…
Descriptors: Computer Science Education, Artificial Intelligence, Cheating, Student Evaluation
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Dawson, Phillip; Sutherland-Smith, Wendy – Assessment & Evaluation in Higher Education, 2019
Contract cheating occurs when students outsource assessed work. In this study, we asked experienced markers from four disciplines to detect contract cheating in a set of 20 discipline-specific assignments. We then conducted a training workshop to improve their detection accuracy, and afterwards asked them to detect contract cheating in 20 new…
Descriptors: Cheating, Accuracy, Training, Evaluators
Sinharay, Sandip – Journal of Educational and Behavioral Statistics, 2018
Wollack, Cohen, and Eckerly suggested the "erasure detection index" (EDI) to detect fraudulent erasures for individual examinees. Wollack and Eckerly extended the EDI to detect fraudulent erasures at the group level. The EDI at the group level was found to be slightly conservative. This article suggests two modifications of the EDI for…
Descriptors: Deception, Identification, Testing Problems, Cheating
Sinharay, Sandip – Grantee Submission, 2017
Wollack, Cohen, and Eckerly (2015) suggested the "erasure detection index" (EDI) to detect fraudulent erasures for individual examinees. Wollack and Eckerly (2017) extended the EDI to detect fraudulent erasures at the group level. The EDI at the group level was found to be slightly conservative. This paper suggests two modifications of…
Descriptors: Deception, Identification, Testing Problems, Cheating
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Jeske, Heimo J.; Lall, Manoj; Kogeda, Okuthe P. – Journal of Information Technology Education: Innovations in Practice, 2018
Aim/Purpose: The aim of this article is to develop a tool to detect plagiarism in real time amongst students being evaluated for learning in a computer-based assessment setting. Background: Cheating or copying all or part of source code of a program is a serious concern to academic institutions. Many academic institutions apply a combination of…
Descriptors: Plagiarism, Identification, Computer Software, Computer Assisted Testing
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Haberman, Shelby J.; Lee, Yi-Hsuan – ETS Research Report Series, 2017
In investigations of unusual testing behavior, a common question is whether a specific pattern of responses occurs unusually often within a group of examinees. In many current tests, modern communication techniques can permit quite large numbers of examinees to share keys, or common response patterns, to the entire test. To address this issue,…
Descriptors: Student Evaluation, Testing, Item Response Theory, Maximum Likelihood Statistics