AI-Powered Classification for Cheating Detection in Offline Examinations Using Deep Learning Techniques with CUI Dataset

Abstract

Supervising students during examinations is a very demanding process, and real-time supervision by human proctors proves to be challenging. This method is time-consuming and involves the extra work of monitoring several students concurrently. Automating exam activity recognition is perceived as one of the ways to address these challenges. In this work, we designed, implemented, and tested an accurate deep learning-based system that classifies student activities during examinations using a pre-trained ResNet50 model. This new concept helps expedite the rate at which exams are monitored and supervised, minimizing the role of human proctors. An amalgamated dataset was used, and the model works with six types of student behaviors, incorporating dropout layers for better performance. The Adam optimizer was employed for fine-tuning the learning process, and k-fold cross-validation was utilized to ensure the model's robustness. The system achieved a high training accuracy of 96%, with 57% of all the documents producing output close to 1 for all categories, demonstrating high precision rates. The results indicate that the proposed method is reliable for future automated proctoring systems. Supervisors can focus on more critical tasks, such as addressing student concerns, rather than constantly monitoring every student's movement. Moreover, the automation system provides consistent and unbiased supervision, eliminating human errors and fatigue that could otherwise affect the monitoring process. This ensures a fairer examination environment where all students are treated equally under constant vigilance. The system can also be scaled to handle larger examination rooms or remote testing, providing flexibility in its deployment.