Cheating Detection for Online Examination Using Clustering Based Approach

Seng Ong - Multimedia University, Jalan Ayer Keroh Lama, 75450, Melaka, Malaysia
Tee Connie - Multimedia University, Jalan Ayer Keroh Lama, 75450, Melaka, Malaysia
Michael Goh - Multimedia University, Jalan Ayer Keroh Lama, 75450, Melaka, Malaysia


Citation Format:



DOI: http://dx.doi.org/10.30630/joiv.7.3-2.2327

Abstract


Online exams have become increasingly popular due to their convenience in eliminating the need for physical exams and allowing students to take exams from remote locations. However, one of the drawbacks of online exams is that they make cheating easier, and it can be difficult for online proctoring to detect subtle movements by the students. This could lead to doubts about students' exam results' value and overall credibility. To address this pressing issue, we present a cheating detection method using a CCTV camera to monitor students' faces, eyes, and devices to determine whether they cheat during exams. If suspicious behavior indicative of cheating is detected, a warning is raised to alert the students. A custom dataset was developed to train the model. The dataset consisted of recordings of pre-determined cheating behavior by 50 participants. These videos captured various poses and behaviors encoded and analyzed using a clustering approach. The encoded clustering method continuously tracks the students' faces, eyes, and body gestures throughout an exam. Experimental results show that the proposed approach effectively detects cheating behavior with a favorable accuracy of 83%. The proposed method offers a promising solution to the growing concern about cheating in online exams. This approach can significantly enhance the integrity and reliability of online assessment processes, fostering trust among educational institutions and stakeholders.

Keywords


Cheating detection; online examination; object detection; clustering; machine learning.

Full Text:

PDF

References


U. Vellappan, L. Lim, and S. Y. Lim, “Engaging Learning Experience: Enhancing Productivity Software Lessons with Screencast Videos,†Journal of Informatics and Web Engineering, vol. 2, no. 2, Art. no. 2, Sep. 2023, doi: 10.33093/jiwe.2023.2.2.14.

I. N. Yulita, F. A. Hariz, I. Suryana, and A. S. Prabuwono, “Educational Innovation Faced with COVID-19: Deep Learning for Online Exam Cheating Detection,†Education Sciences, vol. 13, no. 2, Art. no. 2, Feb. 2023, doi: 10.3390/educsci13020194.

D. M. Cretu and Y.-S. Ho, “The Impact of COVID-19 on Educational Research: A Bibliometric Analysis,†Sustainability, vol. 15, no. 6, Art. no. 6, Jan. 2023, doi: 10.3390/su15065219.

M. Labayen, R. Vea, J. Flórez, N. Aginako, and B. Sierra, “Online Student Authentication and Proctoring System Based on Multimodal Biometrics Technology,†IEEE Access, vol. 9, pp. 72398–72411, 2021, doi: 10.1109/ACCESS.2021.3079375.

R. Wuthisatian, “Student exam performance in different proctored environments: Evidence from an online economics course,†International Review of Economics Education, vol. 35, p. 100196, Nov. 2020, doi: 10.1016/j.iree.2020.100196.

A. W. Muzaffar, M. Tahir, M. W. Anwar, Q. Chaudry, S. R. Mir, and Y. Rasheed, “A Systematic Review of Online Exams Solutions in E-Learning: Techniques, Tools, and Global Adoption,†IEEE Access, vol. 9, pp. 32689–32712, 2021, doi: 10.1109/ACCESS.2021.3060192.

K. Butler-Henderson and J. Crawford, “A systematic review of online examinations: A pedagogical innovation for scalable authentication and integrity,†Computers & Education, vol. 159, p. 104024, Dec. 2020, doi: 10.1016/j.compedu.2020.104024.

S. M. Aslam, A. K. Jilani, J. Sultana, and L. Almutairi, “Feature Evaluation of Emerging E-Learning Systems Using Machine Learning: An Extensive Survey,†IEEE Access, vol. 9, pp. 69573–69587, 2021, doi: 10.1109/ACCESS.2021.3077663.

S. Dendir and R. S. Maxwell, “Cheating in online courses: Evidence from online proctoring,†Computers in Human Behavior Reports, vol. 2, p. 100033, Aug. 2020, doi: 10.1016/j.chbr.2020.100033.

Y. Atoum, L. Chen, A. X. Liu, S. D. H. Hsu, and X. Liu, “Automated Online Exam Proctoring,†IEEE Transactions on Multimedia, vol. 19, no. 7, pp. 1609–1624, Jul. 2017, doi: 10.1109/TMM.2017.2656064.

M. E. Rodríguez, A.-E. Guerrero-Roldán, D. Baneres, and I. Noguera, “Students’ Perceptions of and Behaviors Toward Cheating in Online Education,†IEEE Revista Iberoamericana de Tecnologias del Aprendizaje, vol. 16, no. 2, pp. 134–142, May 2021, doi: 10.1109/RITA.2021.3089925.

S. Mukherjee, B. Rohles, V. Distler, G. Lenzini, and V. Koenig, “The effects of privacy-non-invasive interventions on cheating prevention and user experience in unproctored online assessments: An empirical study,†Computers & Education, vol. 207, p. 104925, Dec. 2023, doi: 10.1016/j.compedu.2023.104925.

S. Kaddoura and A. Gumaei, “Towards effective and efficient online exam systems using deep learning-based cheating detection approach,†Intelligent Systems with Applications, vol. 16, p. 200153, Nov. 2022, doi: 10.1016/j.iswa.2022.200153.

R. Shafique, W. Aljedaani, F. Rustam, E. Lee, A. Mehmood, and G. S. Choi, “Role of Artificial Intelligence in Online Education: A Systematic Mapping Study,†IEEE Access, vol. 11, pp. 52570–52584, 2023, doi: 10.1109/ACCESS.2023.3278590.

M. Garg and A. Goel, “Preserving integrity in online assessment using feature engineering and machine learning,†Expert Systems with Applications, vol. 225, p. 120111, Sep. 2023, doi: 10.1016/j.eswa.2023.120111.

E. F. Okagbue et al., “A comprehensive overview of artificial intelligence and machine learning in education pedagogy: 21 Years (2000–2021) of research indexed in the scopus database,†Social Sciences & Humanities Open, vol. 8, no. 1, p. 100655, Jan. 2023, doi: 10.1016/j.ssaho.2023.100655.

A. Javed and Z. Aslam, “An Intelligent Alarm Based Visual Eye Tracking Algorithm for Cheating Free Examination System,†IJISA, vol. 5, no. 10, pp. 86–92, Sep. 2013, doi: 10.5815/ijisa.2013.10.11.

R. Bawarith, D. A. Basuhail, D. A. Fattouh, and P. D. S. Gamalel-Din, “E-exam Cheating Detection System,†International Journal of Advanced Computer Science and Applications (IJACSA), vol. 8, no. 4, Art. no. 4, 53/29 2017, doi: 10.14569/IJACSA.2017.080425.

M. Ghizlane, B. Hicham, and F. H. Reda, “A New Model of Automatic and Continuous Online Exam Monitoring,†in 2019 International Conference on Systems of Collaboration Big Data, Internet of Things & Security (SysCoBIoTS), Dec. 2019, pp. 1–5. doi: 10.1109/SysCoBIoTS48768.2019.9028027.

A. C. Ozgen, M. U. Öztürk, O. Torun, J. Yang, and M. Z. Alparslan, “Cheating Detection Pipeline for Online Interviews,†in 2021 29th Signal Processing and Communications Applications Conference (SIU), Jun. 2021, pp. 1–4. doi: 10.1109/SIU53274.2021.9477950.

L. C. O. Tiong and H. J. Lee, “E-cheating Prevention Measures: Detection of Cheating at Online Examinations Using Deep Learning Approach -- A Case Study.†arXiv, Jan. 24, 2021. doi: 10.48550/arXiv.2101.09841.

A. Jadi, “New Detection Cheating Method of Online-Exams during COVID-19 Pandemic,†International Journal of Computer Science and Network Security, vol. 21, no. 4, pp. 123–130, Apr. 2021, doi: 10.22937/IJCSNS.2021.21.4.17.

N. Dilini, A. Senaratne, T. Yasarathna, N. Warnajith, and L. Seneviratne, “Cheating Detection in Browser-based Online Exams through Eye Gaze Tracking,†in 2021 6th International Conference on Information Technology Research (ICITR), Dec. 2021, pp. 1–8. doi: 10.1109/ICITR54349.2021.9657277.

A. Barrientos, M. Cuadros, J. Alba, and Ã. S. Cruz, “Implementation of a remote system for the supervision of online exams through the use of cameras with artificial intelligence,†in 2021 IEEE Engineering International Research Conference (EIRCON), Oct. 2021, pp. 1–4. doi: 10.1109/EIRCON52903.2021.9613352.

M. Soltane and M. R. Laouar, “A Smart System to Detect Cheating in the Online Exam,†in 2021 International Conference on Information Systems and Advanced Technologies (ICISAT), Dec. 2021, pp. 1–5. doi: 10.1109/ICISAT54145.2021.9678418.

D. Steffen and A. Chaves Neto, “Ranking Model Applying Self-Organizing Maps and Factor Analysis,†IEEE Latin America Transactions, vol. 19, no. 7, pp. 1217–1224, Jul. 2021, doi: 10.1109/TLA.2021.9461851.

“Gaussian Mixture Model and Self-Organizing Map Neural-Network-Based Coverage for Target Search in Curve-Shape Area | IEEE Journals & Magazine | IEEE Xplore.†Accessed: Oct. 02, 2023. [Online]. Available: https://ieeexplore.ieee.org/document/9208685

“Bias-Corrected Intuitionistic Fuzzy C-Means With Spatial Neighborhood Information Approach for Human Brain MRI Image Segmentation | IEEE Journals & Magazine | IEEE Xplore.†Accessed: Oct. 02, 2023. [Online]. Available: https://ieeexplore.ieee.org/document/9293019

“Solar Radiation Intensity Probabilistic Forecasting Based on K-Means Time Series Clustering and Gaussian Process Regression | IEEE Journals & Magazine | IEEE Xplore.†Accessed: Oct. 02, 2023. [Online]. Available: https://ieeexplore.ieee.org/document/9422819

“Entropic Dynamic Time Warping Kernels for Co-Evolving Financial Time Series Analysis | IEEE Journals & Magazine | IEEE Xplore.†Accessed: Oct. 02, 2023. [Online]. Available: https://ieeexplore.ieee.org/document/9145837

“Dynamic Time Warping Based Adversarial Framework for Time-Series Domain | IEEE Journals & Magazine | IEEE Xplore.†Accessed: Oct. 02, 2023. [Online]. Available: https://ieeexplore.ieee.org/document/9970291