EngageNet: A Model for Evaluating Student Engagement through Facial Expression and Behavior Analysis

Abstract

Online learning has emerged as a prominent trend in modern education, driven by its flexibility, accessibility, and capacity to support personalized learning experiences. However, despite these advantages, one of the most pressing challenges it faces lies in maintaining and accurately evaluating the quality of teaching and learning. A particularly critical aspect is the assessment of learner engagement in virtual environments. In fact, traditional approaches to assessing student engagement, which depend on synchronous, face-to-face interaction, frequently prove inadequate in virtual learning environments where such real-time communication between educators and learners is restricted. Therefore, this study introduces a deep learning-based model that combines facial emotion recognition, gaze direction tracking, and eye openness analysis. By integrating these emotional and behavioral characteristics, the model offers a comprehensive and objective approach to assessing learner’s attention throughout online instruction. To support the development and validation of this model, a specialized dataset was proposed, capturing a diverse range of engagement scenarios. Experimental evaluations demonstrate that the proposed method achieves a notable accuracy of 79.76%, underscoring its effectiveness and robustness in capturing learner engagement dynamics. These findings suggest that the model holds strong potential for enhancing the monitoring and personalization of online learning experiences, thereby contributing to improved educational outcomes in virtual classrooms.