An Experimental Platform for Real-Time Students Engagement Measurements from Video in STEM Classrooms

• Published in: Sensors (Basel, Switzerland) Switzerland), 2023-02, Vol.23 (3), p.1614
• Main Authors: Alkabbany, Islam, Ali, Asem M, Foreman, Chris, Tretter, Thomas, Hindy, Nicholas, Farag, Aly
• Format: Article
• Language: English
• Subjects: Affect (Psychology); Automation; Behavior; behavioral engagement; Biometrics; Classrooms; College students; Education; emotional engagement; Emotions; Engineering; Eye movements; Feedback; Head movement; Humans; Learning; Mathematics; Methods; Real time; School environment; Sensors; student engagement; Student participation; Student retention; Students; Students - psychology; Teaching; Teaching methods; Time measurement
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s23031614

The ability to measure students' engagement in an educational setting may facilitate timely intervention in both the learning and the teaching process in a variety of classroom settings. In this paper, a real-time automatic student engagement measure is proposed through investigating two of the main components of engagement: the behavioral engagement and the emotional engagement. A biometric sensor network (BSN) consisting of web cameras, a wall-mounted camera and a high-performance computing machine was designed to capture students' head poses, eye gaze, body movements, and facial emotions. These low-level features are used to train an AI-based model to estimate the behavioral and emotional engagement in the class environment. A set of experiments was conducted to compare the proposed technology with the state-of-the-art frameworks. The proposed framework shows better accuracy in estimating both behavioral and emotional engagement. In addition, it offers superior flexibility to work in any educational environment. Further, this approach allows a quantitative comparison of teaching methods.