Student Attention Analysis in English Classroom Teaching Based on Feature Selector and Neural Networks

In modern digital education, understanding and enhancing student attention during classroom instruction is essential for improving learning outcomes, especially in language learning contexts. This study proposes a Neural Network-based system for analyzing student attention in English classroom teaching, utilizing real-time data from multimodal sources such as video, audio, and biometric feedback. The system employs a Convolutional Neural Network (CNN) to process facial expressions and gaze direction from video frames, while a Recurrent Neural Network (RNN) tracks temporal patterns of student engagement across the class duration. Audio features, including speech sentiment and teacher-student interaction frequency, are also incorporated to provide a holistic view of the classroom dynamics. A dataset of 500+ annotated classroom sessions was collected from smart classrooms, with expert labels denoting attention levels, interaction quality, and engagement scores. The model achieved an attention detection accuracy of 93.2%, with high correlation to expert evaluations (Pearson's $\mathbf{r}=0.89$). This intelligent analysis framework supports real-time monitoring and postsession feedback in English language classrooms, offering actionable insights to educators and fostering a studentcentered learning environment driven by neural network intelligence.