Analysis of Facial Expression to Estimate the Engagement Level of Students in Online Lectures

Abstract

Abstract

Student engagement is a critical factor in educational effectiveness, particularly in remote and hybrid learning environments. This project presents an AI-based Student Engagement Detection system that utilizes facial analysis and eye tracking to monitor student attention levels in real images. Facial features were analyzed to predict RT to a task-irrelevant stimulus, which was assumed to be an index of the level of attention. We applied a machine learning method. We re-analyzed the data while excluding RT data with sleepy faces of the students to test whether decreased general arousal caused by sleepiness was a significant factor in the RT lengthening observed in the experiment. The results were similar regardless of the inclusion of RTs with sleepy faces, indicating that facial expression can be used to predict learners’ level of attention to video lectures.