Allameh Tabataba’i University Press Journal of Data Science and Modeling 3060-8082 3 1 2024 12 01 CheatingRank: A Multi-Stage Approach for Detecting Cheating in Online Assessments 175 199 20786 10.22054/jdsm.2026.86564.1071 EN Seyede Fatemeh Noorani Department of Information and Computer Engineering, Faculty of Technology and Engineering, Payame Noor University (PNU), P.O. Box 19395-4697, Tehran, Iran Mohammad Reza Mohammadi Department of Information and Computer Engineering, Faculty of Technology and Engineering, Payame Noor University (PNU), P.O. Box 19395-4697, Tehran, Iran Maryam Karimi Department of Computer Science, Faculty of Mathematical Sciences, Shahrekord University, Shahrekord 88186-34141, Iran Nasrin Taherkhani Department of Information and Computer Engineering, Faculty of Technology and Engineering, Payame Noor University (PNU), P.O. Box 19395-4697, Tehran, Iran Journal Article 2025 06 25 In this study, a multi-stage approach based on online exam data analysis was proposed to identify and rank students' Cheating Ranks. Initially, student response sheets were clustered using the $K-means++$ algorithm with dynamic determination of K, forming groups with similar characteristics. Subsequently, in later stages, each student's Cheating Rank was determined based on various behavioral and performance parameters.<br /><br />Results demonstrated that the Cheating Rank derived from online exam data effectively differentiates between students suspicious of cheating and normal students, with statistically significant differences between the two groups. These findings underscore the validity and efficacy of the proposed method in detecting cheating in online exams.<br /><br />Additionally, the impact of threshold selection for group differentiation highlighted the importance of appropriate parameter tuning in enhancing detection accuracy and influencing model sensitivity and reliability. The use of in-person exam scores as a reference criterion strengthened the results' credibility and enabled more objective model evaluation.<br /><br />Given the limitations in sample size and data scope, future research should focus on larger, more diverse, and multidimensional datasets to improve both diagnostic accuracy and model generalizability. Furthermore, integrating this approach with advanced machine learning techniques and behavioral analytics could significantly enhance online exam integrity monitoring systems.<br /><br />Overall, this research represents a significant step toward developing cost-effective, reliable, and efficient methods to reduce cheating in online educational environments, fostering greater trust among instructors and students in assessment processes. https://jdscm.atu.ac.ir/article_20786_6e7c3309fba50a54ac491e0ef66f2341.pdf