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Chang, Shun-Chuan; Chang, Keng Lun – Educational Measurement: Issues and Practice, 2023
Machine learning has evolved and expanded as an interdisciplinary research method for educational sciences. However, cheating detection of test collusion among multiple examinees or sets of examinees with unusual answer patterns using machine learning techniques has remained relatively unexplored. This study investigates collusion on…
Descriptors: Cheating, Identification, Artificial Intelligence, Cooperation
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Riesthuis, Paul; Otgaar, Henry; Hope, Lorraine; Mangiulli, Ivan – Applied Cognitive Psychology, 2022
In the current experiment, we examined the effects of self-generated deceptive behavior on memory. Participants (n = 230) were randomly assigned to a "strong-incentive to cheat" or "weak-incentive to cheat" condition and played the adapted Sequential Dyadic Die-Rolling paradigm. Participants in the "strong-incentive to…
Descriptors: Incentives, Deception, Memory, Cheating
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Zhou, Todd; Jiao, Hong – Educational and Psychological Measurement, 2023
Cheating detection in large-scale assessment received considerable attention in the extant literature. However, none of the previous studies in this line of research investigated the stacking ensemble machine learning algorithm for cheating detection. Furthermore, no study addressed the issue of class imbalance using resampling. This study…
Descriptors: Cheating, Measurement, Artificial Intelligence, Algorithms
Hopper, Zachary Raymond – ProQuest LLC, 2023
As biomedical cognitive enhancement becomes more popular in competitive contexts such as schools, teachers and administrators will face new challenges related to cognitive enhancement and cognitively enhanced students. In this dissertation, I identify five of the most pressing ethical challenges presented by cognitively enhanced students in a…
Descriptors: Biomedicine, Cognitive Ability, Ethics, Drug Therapy
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Meng, Huijuan; Ma, Ye – Educational Measurement: Issues and Practice, 2023
In recent years, machine learning (ML) techniques have received more attention in detecting aberrant test-taking behaviors due to advantages when compared to traditional data forensics methods. However, defining "True Test Cheaters" is challenging--different than other fraud detection tasks such as flagging forged bank checks or credit…
Descriptors: Artificial Intelligence, Cheating, Testing, Information Technology
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Birks, Daniel; Clare, Joseph – International Journal for Educational Integrity, 2023
This paper connects the problem of artificial intelligence (AI)-facilitated academic misconduct with crime-prevention based recommendations about the prevention of academic misconduct in more traditional forms. Given that academic misconduct is not a new phenomenon, there are lessons to learn from established information relating to misconduct…
Descriptors: Artificial Intelligence, Cheating, Student Behavior, Prevention
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Zahrotush Sholikhah; Wiwiek Rabiatul Adawiyah; Bambang Agus Pramuka; Eka Pariyanti – Journal of International Education in Business, 2024
Purpose: Although the academic literature provides extensive insight into the motivations for the unethical use of information technology in online classes, little is known about how perceived justice, the opportunity to cheat and spiritual legitimacy mitigate unethical behavior among young academics. The purposes of this study are two folds:…
Descriptors: Cheating, Electronic Learning, Student Behavior, Religious Factors
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Rick Somers; Sam Cunningham; Sarah Dart; Sheona Thomson; Caslon Chua; Edmund Pickering – IEEE Transactions on Learning Technologies, 2024
Academic misconduct stemming from file-sharing websites is an increasingly prevalent challenge in tertiary education, including information technology and engineering disciplines. Current plagiarism detection methods (e.g., text matching) are largely ineffective for combatting misconduct in programming and mathematics-based assessments. For these…
Descriptors: Assignments, Automation, Identification, Technology Uses in Education
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Giora Alexandron; Aviram Berg; Jose A. Ruiperez-Valiente – IEEE Transactions on Learning Technologies, 2024
This article presents a general-purpose method for detecting cheating in online courses, which combines anomaly detection and supervised machine learning. Using features that are rooted in psychometrics and learning analytics literature, and capture anomalies in learner behavior and response patterns, we demonstrate that a classifier that is…
Descriptors: Cheating, Identification, Online Courses, Artificial Intelligence
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Fatma Basalan Iz; Rahime Aslankoç; Günferah Sahin – Journal of Academic Ethics, 2024
Cheating in higher education is a significant problem. The study aims to determine nursing students' attitudes and opinions toward cheating in exams. The type of research is descriptive. The research data were collected in the classroom environment of 716 students in day and evening education programs. The research data were collected using…
Descriptors: Nursing Education, Student Attitudes, Cheating, Evening Programs
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Xu, Yujun; Li, Wenlong – Journal of Academic Ethics, 2023
This paper provides a systematic and critical review of the existing literature on the phenomenon of 'commercial contract cheating' (CCC). Unlike some existing systematic reviews generally on CCC, this paper focuses on the potential causes and suggested preventative measures specifically, intending to develop effective interventions on the basis…
Descriptors: Prevention, Cheating, Contracts, Outsourcing
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Grochowalski, Joseph H.; Hendrickson, Amy – Journal of Educational Measurement, 2023
Test takers wishing to gain an unfair advantage often share answers with other test takers, either sharing all answers (a full key) or some (a partial key). Detecting key sharing during a tight testing window requires an efficient, easily interpretable, and rich form of analysis that is descriptive and inferential. We introduce a detection method…
Descriptors: Identification, Cooperative Learning, Cheating, Statistical Analysis
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Zhao, Li; Zheng, Yi; Zhao, Junbang; Li, Guoqiang; Compton, Brian J.; Zhang, Rui; Fang, Fang; Heyman, Gail D.; Lee, Kang – Child Development, 2023
Academic cheating is common, but little is known about its early emergence. It was examined among Chinese second to sixth graders (N = 2094; 53% boys, collected between 2018 and 2019) using a machine learning approach. Overall, 25.74% reported having cheated, which was predicted by the best machine learning algorithm (Random Forest) at a mean…
Descriptors: Cheating, Elementary School Students, Artificial Intelligence, Foreign Countries
Leon Katcharian – ProQuest LLC, 2023
Remotely proctored online examinations proliferate in academic and corporate learning environments (Grajek, 2020). Remote (virtual) proctoring allows organizations to efficiently offer tests globally while reducing the costs of proctored testing generally associated with traditional paper-and-pencil and computer-based testing center examinations.…
Descriptors: Computer Assisted Testing, Supervision, Distance Education, Information Security
Kristina M. Brimer – ProQuest LLC, 2023
The study aimed to explore whether cultural dimensions [horizontal individualism (H-I), vertical individualism (V-I), horizontal collectivism (H-C), and vertical collectivism (V-C)] and gender identity predict academic dishonesty tendencies (cheating, copying, collusion) in higher education learners. Using a quantitative approach, participants…
Descriptors: Higher Education, Sexual Identity, Predictor Variables, Cheating
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