Convolutional Neural Network based Behavioural Analysis of English Classroom Teaching for Ecological Education

To effectively enhance students' understanding of environmental protection, schools need to prioritize ecological education by fostering and teaching ecological and low-carbon principles. With the swift advancement of artificial intelligence, deep learning has surfaced as a formidable instrument for analyzing and improving teaching behaviors. This research investigates the behaviour of English teachers within the classroom utilizing deep learning methods to evaluate teaching techniques, student participation, and instructional quality. A dataset comprising video recordings of English lessons was scrutinized using convolutional neural networks (CNNs) and the Attention mechanism to evaluate gestures, speech patterns, and interactions. The findings underscore essential behavioural patterns that affect student participation and understanding. By employing deep learning models, the research offers insights into effective teaching techniques and suggests improvements for English language teaching. The performance metrics indicate that the proposed approach reaches the highest classification accuracy in music genre identification, achieving an accuracy score of 98.3 a precision score of 98. 2%, a recall score of 98. 01%, and an F1 score of 98.11%. The research emphasizes the importance of this framework in differentiating teaching styles and enhancing intelligent educational evaluations.