E-Monitoring of Student Engagement Level using Facial Gestures

Student engagement is a key element to ensure effective learning process. In this work, we presented an automatic system for monitoring level from students’ facial gestures. way, the tutor can analyse of students and improve teaching method strategies enhance There has been extensive research on automated classification level, but most these methods rely mainly expensive eye trackers or physiological sensors in controlled settings. The proposed monitors classifies student based YOLO algorithm by determining gestures, where move freely respond naturally lectures surroundings. model gives mean average precision (mAP) 0.65 complex dataset were allowed during lecture.