Abstract: As digital education continues to rise, the demand for reliable online exam monitoring has grown significantly. Conventional in-person invigilation methods are not feasible in remote settings, highlighting the need for automated proctoring solutions. This paper presents a web-based proctoring system that utilizes real-time webcam monitoring through WebCam.js and server-side functionality powered by Node.js. Instead of relying on artificial intelligence or machine learning, the system adopts a straightforward rule-based approach to identify suspicious behaviors such as switching browser tabs, inconsistent facial presence, and unusual eye movements. A tiered warning system is implemented, where repeated violations lead to automatic termination of the examination session. Built on the MERN stack, the platform emphasizes scalability, user accessibility, and exam integrity. System evaluations indicate high reliability in detecting anomalies, making it an effective alternative to manual supervision.
Keywords: Remote Proctoring, Online Exams, WebCam.js, Node.js, Eye Tracking, Academic Honesty, Cheating Detection.
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DOI:
10.17148/IJARCCE.2025.14418
