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.