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CheatingRank: A Multi-Stage Approach for Detecting Cheating in Online Assessments | ||
| Journal of Data Science and Modeling | ||
| مقالات آماده انتشار، پذیرفته شده، انتشار آنلاین از تاریخ 13 اسفند 1404 اصل مقاله (968.01 K) | ||
| نوع مقاله: Research Manuscript | ||
| شناسه دیجیتال (DOI): 10.22054/jdsm.2026.86564.1071 | ||
| نویسندگان | ||
| Seyede Fatemeh Noorani* 1؛ Mohammad Reza Mohammadi1؛ Maryam Karimi2؛ Nasrin Taherkhani1 | ||
| 1Department of Information and Computer Engineering, Faculty of Technology and Engineering, Payame Noor University (PNU), P.O. Box 19395-4697, Tehran, Iran | ||
| 2Department of Computer Science, Faculty of Mathematical Sciences, Shahrekord University, Shahrekord 88186-34141, Iran | ||
| چکیده | ||
| 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. 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. 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. 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. 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. | ||
| کلیدواژهها | ||
| Cheating Rank؛ Online Examination؛ Clustring | ||
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