Abstract: In the era of digital education, monitoring student engagement has become a critical factor in ensuring effective learning outcomes. This project presents a facial expression-based system designed to analyze and evaluate student engagement in online learning environments. By leveraging computer vision and deep learning techniques, specifically convolutional neural networks (CNNs), the system detects facial expressions and emotional cues through real-time video input from webcams. These expressions are then classified to determine levels of attentiveness, interest, and emotional state, which are key indicators of engagement.
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DOI:
10.17148/IJARCCE.2025.14675
