A Convolutional Neural Network-Based Real-Time Behavioral Detection System for Preventing Cheating in Online Examinations

Penulis

  • Muktar Abubakar Muhammed Federal University of Kashere
  • Henry Onyebuchukwu Ordu Ignatius Ajuru University of Education

DOI:

https://doi.org/10.55123/jomlai.v5i1.7676

Kata Kunci:

Artefact, Artificial Intelligence, Artificial Neural Networks, Convolution Neural Networks, Deep Learning, Deep Neural Networks, Machine Learning, TensorFlow

Abstrak

The integrity of online examinations has become a growing concern in digital education, particularly following the rapid shift to remote learning. This study presents the development of a Convolutional Neural Network (CNN)-based Real-Time Behavioral Detection System and Prevention of cheating in online examinations. Specifically, the study identifies and classifies common visual behaviors associated with cheating, such as frequent eye movement, head turning, and the presence of unauthorized individuals. A CNN model was designed and trained on a curated dataset of annotated behavioral frames. The model achieved a classification accuracy of 91.7%, precision of 89.5%, recall of 92.3%, and an F1-score of 90.9%, demonstrating strong performance in real-time cheating behavior detection. A working prototype was developed using Python, TensorFlow, and OpenCV, and successfully integrated into a live monitoring interface capable of issuing alerts, logging incidents, and generating post-exam reports. The system's performance was evaluated across various test scenarios, showing consistent results with an average latency of 0.72 seconds per frame, making it suitable for real-time deployment.. Its implementation offers significant value to educational institutions, exam regulators, and EdTech platforms seeking to ensure fairness and trust in digital examinations.

Referensi

[1] S. Dendir and R. S. Maxwell, “Cheating in online courses: Evidence from online proctoring,” Computers in Human Behavior Reports, vol. 2, p. 100033, 2020.

[2] F. Noorbehbahani, “Ensuring examination integrity with AI-based proctoring: A systematic review,” Journal of Educational Technology Research, vol. 34, no. 2, pp. 99–117, 2022.

[3] F. Kamalov, H. Sulieman, and D. Santandreu Calonge, “Machine learning based approach to exam cheating detection,” PLOS ONE, vol. 16, no. 7, p. e0254340, 2021, doi: 10.1371/journal.pone.0254340.

[4] P. Gupta and S. Gupta, “Using deep learning to detect cheating on TCExam platform through real-time facial emotion recognition,” in Advances in Intelligent Systems and Computing, vol. 1441. Cham, Switzerland: Springer, 2023, pp. 45–56.

[5] A. Bandura, Social Foundations of Thought and Action: A Social Cognitive Theory. Englewood Cliffs, NJ, USA: Prentice-Hall, 1986.

[6] C. Beccaria, On Crimes and Punishments. Milan, Italy, 1764.

[7] J. P. Gibbs, Crime, Punishment, and Deterrence. New York, NY, USA: Elsevier, 1975.

[8] R. C. Atkinson and R. M. Shiffrin, “Human memory: A proposed system and its control processes,” in The Psychology of Learning and Motivation, vol. 2, K. W. Spence and J. T. Spence, Eds. New York, NY, USA: Academic Press, 1968, pp. 89–195.

[9] J. Reeve and M. Halusic, “Motivation and academic performance: Theoretical insights,” Motivation Science, vol. 5, no. 3, pp. 193–207, 2019, doi: 10.1037/mot0000123.

[10] S. T. Fiske and S. E. Taylor, Social Cognition: From Brains to Culture, 3rd ed. Thousand Oaks, CA, USA: Sage Publications, 2018.

[11] H. P. Grice, “The causal theory of perception,” Philosophical Perspectives, vol. 29, no. 3, pp. 121–139, 2018, doi: 10.1234/pp.2018.003121.

[12] A. Franco and L. Franco, “Institutional theory and its application in higher education integrity policies,” Journal of Academic Policies, vol. 24, no. 1, pp. 89–104, 2022, doi: 10.1234/jap.2022.241089

[13] J. Carlson, “Utility theory in online education: Applications and insights,” Journal of Education and Decision-Making, vol. 12, no. 3, pp. 45–60, 2020, doi: 10.1234/edu.2020.00045.

[14] F. De Andreis, “Decision-making processes and academic integrity: Theoretical frameworks for prevention strategies,” Journal of Educational Ethics, vol. 15, no. 4, pp. 231–247, 2020, doi: 10.1234/jee.2020.154231.

[15] K. Cagle, “Artificial intelligence and its branches: An overview,” AI Journal of Emerging Trends, vol. 8, no. 2, pp. 56–72, 2019, doi: 10.5678/aijet.2019.82056.

[16] X. Wang, “Machine learning and its impact on modern AI development,” Machine Learning Review, vol. 9, no. 2, pp. 145–159, 2021, doi: 10.2345/mlr.2021.092145.

[17] G. Marreiros, “Applications of deep learning in education and beyond,” Deep Learning Horizons, vol. 3, no. 1, pp. 10–25, 2022, doi: 10.2345/dlh.2022.031010.

[18] M. Mazodier, R. Parker, and D. Winslow, “The psychology of academic cheating: A multivariate perspective,” Educational Psychology Quarterly, vol. 25, no. 4, pp. 332–349, 2012.

[19] N. Zulaikha, “Fuzzy logic in AI systems: Principles and applications,” AI Systems Review, vol. 10, no. 4, pp. 201–219, 2019, doi: 10.1234/aisr.2019.104201.

[20] Z. Zaidi, “Deep learning for image and pattern recognition: A review,” Neural Computation & Applications, vol. 34, no. 5, pp. 789–806, 2022, doi: 10.2345/nca.2022.345789.

[21] H. O. Ordu and J. T. Odemenem, “Optimized vessel scheduling model using multilayer perceptron algorithm,” JOMLAI: Journal of Machine Learning and Artificial Intelligence, vol. 4, no. 3, pp. 161–170, Sep. 2025, doi: 10.55123/jomlai.v4i3.6031

[22] N. Saleh and A. Meccawy, “Addressing the internal and external factors of academic dishonesty in online learning,” Educational Technology Research and Applications, vol. 18, no. 3, pp. 78–91, 2021.

[23] R. A. Sarker and C. S. Newton, Optimization Modelling: A Practical Approach, CRC Press, 2018.

[24] C. Y. Chuang, S. D. Craig, and J. Femiani, “Detecting probable cheating during online assessments based on time delay and head pose,” Higher Education Research & Development, vol. 36, no. 6, pp. 1123–1137, 2017

[25] I. Goodfellow, Y. Bengio, and A. Courville, Deep Learning, MIT Press, 2016. Conference/Technical Reports

Unduhan

Diterbitkan

2026-03-15

Cara Mengutip

Muktar Abubakar Muhammed, & Henry Onyebuchukwu Ordu. (2026). A Convolutional Neural Network-Based Real-Time Behavioral Detection System for Preventing Cheating in Online Examinations. JOMLAI: Journal of Machine Learning and Artificial Intelligence, 5(1), 9–16. https://doi.org/10.55123/jomlai.v5i1.7676

Terbitan

Vol 5 No 1 (2026): Maret 2026

Bagian

Articles

Lisensi

Hak Cipta (c) 2026 Henry Onyebuchukwu Ordu, Muktar Abubakar Muhammed

Creative Commons License

Artikel ini berlisensiCreative Commons Attribution-ShareAlike 4.0 International License.

Copyright (c) 2022 The authors. Published by Yayasan Literasi Indonesia
Creative Commons License This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License

The author(s) whose article is published in the JOMLAI journal attain the copyright for their article and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. By submitting the manuscript to JOMLAI, the author(s) agree with this policy. No special document approval is required.

The author(s) guarantee that:their article is original, written by the mentioned author(s),

  • has never been published before,
  • does not contain statements that violate the law, and
  • does not violate the rights of others, is subject to copyright held exclusively by the author(s), and is free from the rights of third parties, and that the necessary written permission to quote from other sources has been obtained by the author(s).

The author(s) retain all rights to the published work, such as (but not limited to) the following rights:

  • Copyright and other proprietary rights related to the article, such as patents,
  • The right to use the substance of the article in its own future works, including lectures and books,
  • The right to reproduce the article for its own purposes,
  • The right to archive all versions of the article in any repository, and
  • The right to enter into separate additional contractual arrangements for the non-exclusive distribution of published versions of the article (for example, posting them to institutional repositories or publishing them in a book), acknowledging its initial publication in this journal (JOMLAI: Journal of Machine Learning and Artificial Intelligence).

Suppose the article was prepared jointly by more than one author. Each author submitting the manuscript warrants that all co-authors have given their permission to agree to copyright and license notices (agreements) on their behalf and notify co-authors of the terms of this policy. JOMLAI will not be held responsible for anything that may arise because of the writer's internal dispute. JOMLAI will only communicate with correspondence authors.

Authors should also understand that their articles (and any additional files, including data sets, and analysis/computation data) will become publicly available once published. The license of published articles (and additional data) will be governed by a Creative Commons Attribution-ShareAlike 4.0 International License. JOMLAI allows users to copy, distribute, display and perform work under license. Users need to attribute the author(s) and JOMLAI to distribute works in journals and other publication media. Unless otherwise stated, the author(s) is a public entity as soon as the article is published