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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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Karnalim, Oscar; Simon; Chivers, William – IEEE Transactions on Learning Technologies, 2023
We have recently developed an automated approach to reduce students' rationalization of programming plagiarism and collusion by informing them about the matter and reporting uncommon similarities to them for each of their submissions. Although the approach has benefits, it does not greatly engage students, which might limit those benefits. To…
Descriptors: Gamification, Programming, Plagiarism, Cooperative Learning
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Lemantara, Julianto; Hariadi, Bambang; Sunarto, M. J. Dewiyani; Amelia, Tan; Sagirani, Tri – IEEE Transactions on Learning Technologies, 2023
A quick and effective learning assessment is needed to evaluate the learning process. Many tools currently offer automatic assessment for subjective and objective questions; however, there is no such free tool that provides plagiarism detection among students for subjective questions in a learning management system (LMS). This article aims to…
Descriptors: Students, Cheating, Prediction, Essays
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Jiang, Zhuhan; Huang, Jiansheng – IEEE Transactions on Learning Technologies, 2022
Advanced digital technologies and social media have greatly improved both the learning experience and the assessment convenience, while inadvertently facilitated potential plagiarism and collaborative cheating at the same time. In this article, we will focus on the strategies and their technological implementations to run exams, or in-class tests…
Descriptors: Plagiarism, Educational Technology, Computer Assisted Testing, Cheating
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Chad C. Tossell; Nathan L. Tenhundfeld; Ali Momen; Katrina Cooley; Ewart J. de Visser – IEEE Transactions on Learning Technologies, 2024
This article examined student experiences before and after an essay writing assignment that required the use of ChatGPT within an undergraduate engineering course. Utilizing a pre-post study design, we gathered data from 24 participants to evaluate ChatGPT's support for both completing and grading an essay assignment, exploring its educational…
Descriptors: Student Attitudes, Computer Software, Artificial Intelligence, Grading