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Abstract This paper presents a groundbreaking online educational platform that utilizes facial expression recognition technology to track the progress of students within the classroom environment. Through periodic image capture and facial data extraction, the platform employs ResNet50, CBAM, and TCNs for enhanced facial expression recognition. Achieving accuracies of 91.86%, 91.71%, 95.85%, and 97.08% on the RAF-DB, FER2013, CK + , and KDEF expression datasets, respectively, the proposed model surpasses the initial ResNet50 model in accuracy and detection of students' learning states. Comparative evaluations against state-of-the-art models using the RAF-DB, FER2013, CK + , and KDEF datasets underscore the significance of the results for educational institutions. By enhancing emotion recognition accuracy, improving feature relevance, capturing temporal dynamics, enabling real-time monitoring, and ensuring robustness and adaptability in online classroom environments, this approach offers valuable insights for educators to enhance teaching strategies and student outcomes. The combined capabilities of ResNet50, CBAM, and TCNs contribute uniquely to capturing dynamic changes in facial expressions over time, thereby facilitating accurate interpretation of students' emotions and engagement levels for more effective monitoring of learning behaviors in real-time.
Mohammed Aly (2024) studied this question.