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…

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…

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…

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…

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…

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…

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:…

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…

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…

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…

Academic misconduct and academic integrity are issues of importance to Higher Education Institutions (HEIs). Phraseologies and practices may conflate unintentional mistakes with attempts to gain illegitimate advantage, with some groups potentially at higher risk. HEIs across the United Kingdom (UK) responded to a Freedom of Information Act (FOI)…

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…

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…

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…

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.…