Enhancing E-Learning with Face Recognition and Linear Regression Analysis

During the COVID-19 pandemic, online tools became essential due to physical restrictions, but they brought communication challenges and hindered learning. Students' engagement declined as they turned to social media during online lectures. To address this, a desktop application utilizing the YOLO v8 model is proposed. This highly efficient face recognition algorithm detects and recognizes faces in real-time, automating attendance monitoring. By reducing distractions and improving monitoring, the application aims to enhance the teaching experience. This paper focuses on implementing this solution to improve learning, analyzing the relationship between student attendance and exam scores using collected data from the Face recognition Attendance Monitoring module.