Real Time Online Exam Monitor Management System in Educational Institutions

Abstract

Maintaining academic integrity in online examinations is a critical challenge within digital learning environments. This research presents an integrated, multimodal authentication system designed to detect and prevent cheating in real-time during remote assessments. The system combines secure identity verification, continuous facial recognition, audio analysis, and object detection to monitor student behavior comprehensively throughout the exam session. Before exam access, students undergo a rigorous identity verification process using pre-registered biometric data. During the exam, facial recognition continuously confirms the presence of the authenticated student. At the same time, audio classification identifies suspicious sounds such as whispering, paper rustling, or electronic device usage that may indicate unauthorized assistance. Simultaneously, object detection scans the video feed for prohibited items such as mobile phones or notes. All detected irregularities are immediately recorded in a real-time database, complete with timestamps and screenshots to serve as documented evidence. Repeated or prolonged violations trigger automatic flags for potential misconduct, facilitating review by academic integrity committees. The study sample consisted of 30 first-year Computer Department students from the Faculty of Specific Education at Mansoura University. Statistical analysis revealed a highly significant difference (p < 0.01) in students’ achievement scores before and after the system’s implementation, with higher scores observed before using the monitoring system. The large effect size (0.677) underscores the system’s effectiveness in curbing cheating behaviors. Additionally, a reduction in score variability post-implementation suggests increased homogeneity and a general decline in performance, likely reflecting the system’s stringent monitoring and its impact on limiting dishonest practices. By automating the detection of academic dishonesty and reducing reliance on human proctors, this system offers a scalable and reliable solution for enhancing the fairness and credibility of online examinations.