Privacy-Preserving On-Screen Activity Tracking in E- Learning

The rapid growth of online education has made effective proctoring systems essential to maintaining academic

integrity and ensuring fair exams and online learning. Conventional proctoring methods like in-person invigilation are not

feasible for online exams and e-learning. To address this issue, an automated proctoring method based on computer vision is

proposed. This system tracks and analyzes applicant behavior throughout online learning and testing using advanced computer

vision algorithms. A webcam and microphone are used by the Automated Proctoring System to continuously monitor the test

environment. The Automated Proctoring System uses a webcam and screen recorder to monitor the test environment in real

time. Computer vision algorithms are used to identify and monitor the examinee's face, gaze direction, eye movements, body

posture, and suspicious activities. Machine learning models identify unusual patterns of behavior, such as continually turning

away from the screen or showing many faces in the camera frame. These abnormalities cause the examiners to receive alerts for

further inquiry. The proposed method aims to enhance exam security and e-learning by mitigating academic dishonesty,

including cheating, impersonation, and unauthorized aids. It uses computer vision to provide a scalable and non-intrusive

method of monitoring online assessments and e-learning