Facial Expression Recognition with Vision Transformers for Intelligent Engagement Monitoring in English Classroom

For better learning outcomes and teaching efficacy in today's schools, it is essential to comprehend student engagement and emotional reactions. Conventional teaching evaluation techniques frequently depend on subjective observation and delayed feedback, which might not fully capture students' in-the-moment learning experiences. Using deep learning-based facial expression recognition enabled by Vision Transformers (ViT), this study offers a method for assessing student engagement and emotional response in English classrooms. The facial expression features are modelled with high accuracy and robustness under various lighting and pose conditions using Vision Transformers, which are renowned for their exceptional performance on image classification tasks. Through the integration of these insights with frameworks for teaching evaluation, the model provides educators with real-time feedback on the effectiveness of their instruction, allowing them to make dynamic adjustments to their teaching methods. According to the performance metrics, the suggested method attains the highest accuracy in the Intelligent Dancing Teaching Support System, with 98.5% accuracy, 98.52% precision, 98.51% recall, and 98.52% F1 score. In the context of teaching English, this AI-powered feedback loop encourages a more dynamic and sensitive learning environment.