Intelligent Analysis of English Teaching Behavior in Classrooms Using Deep Learning

Over the years, the use of artificial intelligence (AI) in educational settings has picked up in momentum, especially in the automated observation of teaching behavior in classrooms. Teacher performance monitoring and assessment in class lessons are essential to improve pedagogical efficiency, student interaction, and organizational accountability. Conventional observation-dependent methods are typically subjective, time-intensive, and scale-constrained. Whereas initial AI-powered systems employed simple machine learning methods and rule-based video analysis for detecting behavior, such methods fail to capture the intricate spatio-temporal patterns of real teaching practices and have low accuracy with poor generalization across various classroom contexts. To address such limitations, this research proposes a hybrid deep learning model is used to temporal sequence, and an Attention Mechanism to dynamically concentrate on the most meaningful behavioral frames. The proposed model is trained and tested on a classroom dataset. Experimental outcomes show that the introduced model clearly outperforms basic architectures like RNNs, LSTMs, and GRUs with an overall accuracy of 96.6 %, also having better precision, recall, and F1-score values. This system provides a strong, scalable solution for objective and smart analysis of English teachers' classroom teaching behavior, which can drive the development of AI-assisted teacher evaluation systems for data-driven pedagogical improvements.