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Tiana P. Johnson-Clements; Guy J. Curtis; Joseph Clare – Journal of Academic Ethics, 2025
Concerns over students engaging in various forms of academic misconduct persist, especially with the post-COVID-19 rise in online learning and assessment. Research has demonstrated a clear role of the personality trait psychopathy in cheating, yet little is known about why this relationship exists. Building on the research by Curtis et al.…
Descriptors: Pandemics, COVID-19, Cheating, Electronic Learning
Sun-Joo Cho; Goodwin Amanda; Jorge Salas; Sophia Mueller – Grantee Submission, 2025
This study incorporates a random forest (RF) approach to probe complex interactions and nonlinearity among predictors into an item response model with the goal of using a hybrid approach to outperform either an RF or explanatory item response model (EIRM) only in explaining item responses. In the specified model, called EIRM-RF, predicted values…
Descriptors: Item Response Theory, Artificial Intelligence, Statistical Analysis, Predictor Variables
Necati Taskin; Mehmet Kokoç – Education and Information Technologies, 2025
This study examines the growing issue of academic dishonesty in online assessments, a challenge intensified by the expansion of digital learning platforms. Specifically, it investigates the relationship between students' online engagement and their performance in online versus traditional paper-and-pencil tests. Employing a cross-sectional…
Descriptors: Cheating, Ethics, Computer Assisted Testing, Technology Uses in Education
Laura L. Beaton – Journal of Educational Technology Systems, 2025
Online quizzes and learning platforms provided by textbook publishers have become common components of undergraduate education. Here, I examine how participation in these formative assessments related to student course performance. Over multiple semesters, students completed either free online unlimited attempt quizzes or assignments from a…
Descriptors: Formative Evaluation, Computer Assisted Testing, Tests, Student Evaluation
Thao-Trang Huynh-Cam; Long-Sheng Chen; Tzu-Chuen Lu – Journal of Applied Research in Higher Education, 2025
Purpose: This study aimed to use enrollment information including demographic, family background and financial status, which can be gathered before the first semester starts, to construct early prediction models (EPMs) and extract crucial factors associated with first-year student dropout probability. Design/methodology/approach: The real-world…
Descriptors: Foreign Countries, Undergraduate Students, At Risk Students, Dropout Characteristics

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