Detecting online test cheating through user behavior monitoring

Ooi, Khai Shen (2025) Detecting online test cheating through user behavior monitoring. Final Year Project, UTAR.

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Abstract

The shift to online testing is becoming a significant trend in the modern learning, yet this transition presents serious challenges to academic integrity and credibility of institutional qualifications. In a traditional test environment, physical supervision like physical examination setup in grand hall and attendance is a must, effectively detect cheating, but such monitoring is not feasible for remote exams, creating opportunities for students to engage in dishonest behaviour. The proposed system, "EyeGuard," aims to assist and solve this issue by employing a computer vision-based eye gaze detection system that uses a student webcam to track their eye movements during an online test. By analysing eye gaze patterns and browser incident to identify inappropriate activity, such as looking at unauthorized materials during the test and switching the tab, the system can detect potential cheating in real-time, allowing any suspicious behaviour to be reported instantly for necessary investigation. The principal objective of this project is to provide a reliable and effective solution for monitoring online exams that reduces the reliance on human supervisors, which can be costly and impractical at scale. Through the automated detection of suspicious behaviour, "EyeGuard" fosters a more confident and fairer environment for online test, ensuring the integrity of the examination process while offering a scalable, and low-cost solution for educational institutions.

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