Facial Recognition System to Detect Student Emotions and Cheating in Distance Learning

Distance learning has spread nowadays on a large scale across the world, which has led to many challenges in education such as invigilation and learning coordination. These challenges have attracted the attention of many researchers aiming at providing high quality and credibility monitoring of students. Distance learning has offered an effective education alternative to traditional learning in higher education. The lecturers in universities face difficulties in understanding students’ emotions and abnormal behaviors during educational sessions and e-exams. The purpose of this study is to use computer vision algorithms and deep learning algorithms to develop a new system that supports lecturers in monitoring and managing students during online learning sessions and e-exams. To achieve the proposed objective, the system employs software methods, computer vision algorithms, and deep learning algorithms. Semi-structural interviews were also used as feedback to enhance the system. The findings showed that the system achieved high accuracy for student identification in real time, student follow-up during the online session, and cheating detection. Future work can focus on developing additional tools to assist students with special needs and speech recognition to improve the follow-up facial recognition system’s ability to detect cheating during e-exams in distance learning.