Abstract
Academic misconduct is a growing problem in online education. While there are ways to curb academic misconduct in online exams, utilization of technology to proctor online exams in a simple manner in limited-resource settings remain unclear. This study set out to identify a reliable technique for utilizing webcam footage to identify instances of academic dishonesty. Instead of applying component-based feature extraction, this study provides a deep learning-based approach to online exam proctoring that recognizes exam candidates’ behaviors directly from video input. By using an online exam dataset consisting of twenty-four test-takers who replicate real-world actions, experimental results show the efficiency of the approach. The article provides a collection of webcam recordings with annotated actions to evaluate the proposed approach. The findings show that a deep learning model using the Slow Only variation has a true detection rate of 78.9%. The visualization module reduces the amount of time invigilators must spend watching videos to capture academic misbehaviors by offering a comprehensive graphical view with details of a candidate’s actions during an exam. The study’s conclusions will help design an effective and efficient system for online exam proctoring in resource-constrained environments.
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The participants of this study did not give written consent for their data to be shared publicly, so due to the sensitive nature of the research supporting data is not available.
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Felsinger, D., Halloluwa, T. & Fonseka, I. Video based action detection for online exam proctoring in resource-constrained settings. Educ Inf Technol 29, 12077–12091 (2024). https://doi.org/10.1007/s10639-023-12385-1
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DOI: https://doi.org/10.1007/s10639-023-12385-1