Deep Learning Classroom Management System for Exam Monitoring and Student Behavior Detection

Cellphone detection in online exams is crucial for maintaining academic integrity by preventing the use of unauthorized devices. It ensures fairness among students, safeguards exam security, and adheres to regulatory standards, ultimately promoting a fair evaluation process. Designing a system for monitoring student behavior in a classroom during class hours, focusing on detecting abnormal actions such as jumping on tables or physical altercations. Using sensors like cameras and intelligent algorithms, the system should analyze real-time classroom activity and send alerts to the teacher or administration when unusual behavior is detected. The system must prioritize accurate behavior detection while respecting student privacy and ethical considerations. It should also integrate seamlessly into the classroom environment without disrupting learning. Developing a system for monitoring student behavior during online exams, focusing on detecting suspicious head movements that may indicate cheating. Utilizing cameras and computer vision algorithms, the system will analyze real-time student behavior and alert proctors or educators when suspicious movements are detected. The system should prioritize accuracy in identifying cheating while respecting privacy and ethical considerations. Additionally, it must operate smoothly within the existing online exam infrastructure without interrupting the examination process.