A Machine Learning Approach for Quantifying Academic Misconduct

Year 2024, , 188 - 198, 31.12.2024

Almasi Maguya

https://doi.org/10.26650/acin.1557985

Abstract

Evidence from the literature continues to reveal the problem of academic misconduct, particularly cheating, among university students. To deal with this problem effec tively, a clear understanding of its magnitude is necessary for planning and resource allocation. This paper proposes a machine learning algorithm to quantify the mag nitude of academic misconduct among undergraduate students. In this study, cluster analysis was employed with outlier detection and removal. The algorithm was trained on a dataset comprising 678 short texts. Results indicated that over 80% of students engage in the practice of academic misconduct. This shows that academic misconduct among undergraduate students poses a serious risk to the quality of graduates. This paper proposes a machine learning algorithm to quantify academic misconduct. The proposed algorithm is based on a modified k-means clustering algorithm that auto matically detects and removes outliers. Universities can adopt the proposed method to combat the growing problem of academic misconduct among undergraduate stu dents. The proposed approach for quantifying the magnitude of academic misconduct is more reliable and cost-effective than traditional (survey-based) methods.

Keywords

Unsupervised machine learning, machine learning, academic dishonesty, clustering, outlier removal

References

• Aggarwal, C. (2022). Machine learning for text. Springer International Publishing. google scholar Akiful, H. A., Roy, K., Abdullah, N., Priota, N. Z., & Onim, S. H. (2022). Performance Analysis of Machine Learning Models for Cheating Detection in Online Examinations. In 2022 25th international conference on computer and information technology (ICCIT) (pp. 342-347). doi: 10.1109/ICCIT57492.2022.10055801 google scholar
• Anitha, P., & Sundaram, S. (2021). Prevalence, types and reasons for academic dishonesty among college students. Journal of Studies in Social Sciences and Humanities, 7(1), 1-14. google scholar
• Awdry, R. (2021). Assignment outsourcing: Moving beyond contract cheating. Assessment & Evaluation in Higher Education, 46 (2), 220-235. doi: 10.1080/02602938.2020.1765311 google scholar
• Bernius, J. P., Krusche, S., & Bruegge, B. (2022). Machine learning based feedback on textual student answers in large courses. Computers and Education: Artificial Intelligence, 3, 100081. doi: https://doi.org/10.1016/j.caeai.2022.100081 google scholar
• Carpenter, D. D., Harding, T. S., Finelli, C. J., & Passow, H. J. (2004). Does academic dishonesty relate to unethical behavior in professional practice? An exploratory study. Science and engineering ethics, 10, 311-324. google scholar
• Chala, W. D. (2021). Perceived seriousness of academic cheating behaviors among undergraduate students: an Ethiopian experience. International Journal for Educational Integrity, 17(1), 2. google scholar
• Chang, S.-C., & Chang, K. L. (2023). Cheating Detection of Test Collusion: A Study on Machine Learning Techniques and Feature Representation. Educational Measurement: Issues and Practice, 42 (2), 62-73. doi: https://doi.org/10.1111/emip.12538 google scholar
• Clare, J., Walker, S., & Hobson, J. (2017). Can we detect contract cheating using existing assessment data? Applying crime prevention theory to an academic integrity issue. International Journal for Educational Integrity, 13(1), 1-15. google scholar
• DiPaulo, D. (2022a). Do preservice teachers cheat in college, too? A quantitative study of academic integrity among preservice teachers. International Journal for Educational Integrity, 18(1), 2. google scholar
• DiPaulo, D. (2022b). Do preservice teachers cheat in college, too? A quantitative study of academic integrity among preservice teachers. International Journal for Educational Integrity, 18(1), 2. google scholar
• Fontaine, S., Frenette, E., & Hébert, M.-H. (2020). Exam cheating among Quebec’s preservice teachers: the influencing factors. International Journal for Educational Integrity, 16(1), 1-18. google scholar
• Gallant, T. B., & Drinan, P. (2006). Organizational theory and student cheating: Explanation, responses, and strategies. The Journal of Higher Education, 77 (5), 839-860. google scholar
• Grenness, T. (2023). ”If You Don’t Cheat, You Lose”: An Explorative Study of Business Students’ Perceptions of Cheating Behavior. Scandinavian Journal of Educational Research, 67(7), 1122-1136. doi: 10.1080/00313831.2022.2116479 google scholar
• Jenkins, B. D., Golding, J. M., Le Grand, A. M., Levi, M. M., & Pals, A. M. (2023). When opportunity knocks: College students’ cheating amid the COVID-19 pandemic. Teaching of Psychology, 50(4), 407-419. google scholar
• Kaddoura, S., & Gumaei, A. (2022). Towards effective and efficient online exam systems using deep learning-based cheating detection approach. Intelligent Systems with Applications, 16 , 200153. doi: https://doi.org/10.1016/j.iswa.2022.200153 google scholar
• Kamalov, F., Sulieman, H., & Santandreu Calonge, D. (2021). Machine learning based approach to exam cheating detection. Plos one, 16(8), e0254340. google scholar
• Khabbachi, I., Zouhair, A., Mahboub, A., & Elghouch, N. (2023). Reduce Cheating in e-Exams Using Machine Learning: State of the Art. In M. Lazaar, E. M. En-Naimi, A. Zouhair, M. Al Achhab, & O. Mahboub (Eds.), Proceedings of the 6th international conference on big data and internet of things (pp. 225-238). Springer International Publishing. google scholar
• Lancaster, T., & Cotarlan, C. (2021). Contract cheating among STEM students through file sharing websites: A COVID-19 pandemic perspective. International Journal for Educational Integrity, 17(1), 1-16. google scholar
• Locquiao, J., & Ives, B. (2020). First-year university students’ knowledge of academic misconduct and the association between goals for attending university and receptiveness to intervention. International Journal for Educational Integrity, 16(1), 5. google scholar
• Lund, B. D., Wang, T., Mannuru, N. R., Nie, B., Shimray, S., & Wang, Z. (2023). ChatGPT and a new academic reality: Artificial Intelligence-written research papers and the ethics of the large language models in scholarly publishing. Journal of the Association for Information Science and Technology, 74(5), 570-581. google scholar
• Malik, A. A., Hassan, M., Rizwan, M., Mushtaque, I., Lak, T. A., & Hussain, M. (2023). Impact of academic cheating and perceived online learning effectiveness on academic performance during the COVID-19 pandemic among Pakistani students. Frontiers in Psychology, 14(2), 1124095. google scholar
• Meng, H., & Ma, Y. (2023). Machine Learning-Based Profiling in Test Cheating Detection. Educational Measurement: Issues and Practice, 42(1), 59-75. doi: https://doi.org/10.1111/emip.12541 google scholar
• Newton, P. M., & Essex, K. (2023). How common is cheating in online exams and did it increase during the COVID-19 pandemic? A Systematic Review. Journal of Academic Ethics, 1-21. google scholar
• Nigam, A., Pasricha, R., Singh, T., & Churi, P. (2021). A systematic review on AI-based proctoring systems: Past, present and future. Education and Information Technologies, 26 (5), 6421-6445. google scholar
• Nonis, S., & Swift, C. O. (2001). An examination of the relationship between academic dishonesty and workplace dishonesty: A multicampus investigation. Journal of Education for business, 77(2), 69-77. google scholar
• Noorbehbahani, F., Mohammadi, A., & Aminazadeh, M. (2022). A systematic review of research on cheating in online exams from 2010 to 2021. Education and Information Technologies, 27(6), 8413-8460. google scholar
• Orok, E., Adeniyi, F., Williams, T., Dosunmu, O., Ikpe, F., Orakwe, C., & Kukoyi, O. (2023). Causes and mitigation of academic dishonesty among healthcare students in a Nigerian university. International Journal for Educational Integrity, 19(1), 13. google scholar
• Peres, R., Schreier, M., Schweidel, D., & Sorescu, A. (2023). On ChatGPT and beyond: How generative artificial intelligence may affect research, teaching, and practice. International Journal of Research in Marketing. doi: https://doi.org/10.1016/j.ijresmar.2023.03.001 google scholar
• Pino, N. W., & Smith, W. L. (2003). College students and academic dishonesty. College Student Journal, 37(4), 490-500. google scholar
• Ranger, J., Schmidt, N., & Wolgast, A. (2022). Detecting Cheating in Large-Scale Assessment: The Transfer of Detectors to New Tests. Educational and Psychological Measurement, 0(0), 00131644221132723. doi: 10.1177/00131644221132723 google scholar
• Renzella, J., Cain, A., & Schneider, J.-G. (2022). Verifying student identity in oral assessments with deep speaker. Computers and Education: Artificial Intelligence, 3 , 100044. doi:10.1016/j.caeai.2021.100044 google scholar
• Rettinger, D., & Kramer, Y. (2009). Situational and Personal Causes of Student Cheating. Research in Higher Education, 50 , 293-313. doi: https://doi.org/10.1007/s11162-008 -9116-5 google scholar
• Salehi, M., & Gholampour, S. (2021). Cheating on exams: Investigating Reasons, Attitudes, and the Role of Demographic Variables. SAGE Open, 11(2), 21582440211004156. doi: 10.1177/21582440211004156 google scholar
• Simon, C. A., Carr, J. R., McCullough, S. M., Morgan, S. J., Oleson, T., & Ressel, M. (2003). The other side of academic dishonesty: The relationship between faculty scepticism, gender and strategies for managing student academic dishonesty cases. Assessment & Evaluation in Higher Education, 28(2), 193-207. google scholar
• Swiecki, Z., Khosravi, H., Chen, G., Martinez-Maldonado, R., Lodge, J. M., Milligan S. . . Ga'sevi'c, D. (2022). Assessment in the age of artificial intelligence. Computers and Education: Artificial Intelligence, 3 , 100075. doi:10.1016/j.caeai.2022.100075 google scholar
• Uzun, L. (2023). ChatGPT and academic integrity concerns: Detecting artificial intelligence generated content. Language Education and Technology, 3(1), 45-54. google scholar
• Waltzer, T., & Dahl, A. (2023). Why do students cheat? Perceptions, evaluations, and motivations. Ethics & Behavior , 33 (2), 130-150. doi: 10.1080/10508422.2022.2026775 google scholar
• Wang, Y., & Xu, Z. (2021). Statistical Analysis for Contract Cheating in Chinese Universities. Mathematics, 9 (14). doi: 10.3390/math9141684 google scholar
• Zhao, L., Peng, J., Dong, L. D., Compton, B. J., Zhong, Z., Li Y. . . Lee, K. (2023). Academic cheating interferes with learning among middle school students. Journal of Experimental Child Psychology, 226(2), 10556. google scholar