Facial Expression Recognition for Monitoring Learning Satisfaction in Smart Learning Environments Using MobileNetV2

This study develops a lightweight, privacy-aware Facial Expression Recognition (FER) framework to monitor learning satisfaction in Smart Learning Environments (SLEs). Using MobileNetV2 with a two-stage training scheme on the FER2013 dataset and evaluated on 35,000 test samples, the system addresses two main questions: (1) how effectively a customized MobileNetV2 recognizes core student expressions under authentic classroom conditions, and (2) how temporal aggregation and confidence calibration improve the stability of a Learning Satisfaction Index (LSI). The model achieves 0.39 accuracy and 0.34 macro-F1, with strong performance for happy, neutral, and surprise, while challenges remain for fear–surprise and neutral–sad. Temporal smoothing reduces prediction noise and enhances the reliability of LSI signals for instructional decision-making. The findings highlight practical implications for education, particularly in supporting real-time formative assessment and improving teachers’ awareness of student engagement through privacy-preserving, on-device affect monitoring.