Measuring Student Engagement Level Using Facial Information

In this paper, we propose a novel framework that measures the engagement level of students either in a class environment or in an e-learning environment. The proposed framework captures the user’s video and tracks their faces’ through the video’s frames. Different features are extracted from the user’s face e.g., facial fiducial points, head pose, eye gaze, learned features, etc. These features are then used to detect the Facial Action Coding System (FACS), which decomposes facial expressions in terms of the fundamental actions of individual muscles or groups of muscles (i.e., action units). The decoded action units (AU’s) are then used to measures the student’s willingness to participate in the learning process (i.e., behavioral engagement) and his/her emotional attitude towards learning (i.e., emotional engagement). This framework will allow the lecturer to receive a real-time feedback from facial features, gaze, and other body kinesics. The framework is robust and can be utilized in numerous applications including but not limited to the monitoring the progress of students with various degrees of learning disabilities, and the analysis of nerve palsy and its effects on facial expression and social interactions.