A transformer based approach to STEAM integrated english course design in high schools under deep learning

This study explores the integration of the educational philosophy of Science, Technology, Engineering, Arts, and Mathematics (STEAM) with deep learning technologies for the development of high school English course resources. By introducing deep learning models, particularly the Transformer architecture, the study enables automatic generation and personalized delivery of instructional content, thereby enhancing the intelligence and relevance of course resources. An innovative Transformer-based framework incorporating STEAM-related semantic features is designed and implemented. The framework is trained on a large-scale, preprocessed English text dataset collected from 300 high school students participating in STEAM-integrated English learning tasks, with data from 150 students forming the core corpus for model training. The model employs an encoder-decoder architecture and is optimized using the Adam optimizer to support the generation of high-quality content tailored to diverse teaching objectives. Through on-site teaching experiments conducted with the 300 high school students, the results demonstrate that, the model's performance is rigorously evaluated through metrics such as Bilingual Evaluation Understudy score (0.78), innovation index (0.75), and content diversity (0.70). Results reveal that the proposed model outperforms comparative models (Model A and Model B) in language generation accuracy, innovation, and content diversity, highlighting its superior capability in producing high-quality English teaching resources. Although the model demonstrates promising results, its implementation in resource-limited environments presents challenges, highlighting the need for further refinements. Nonetheless, these findings demonstrate the model's potential to revolutionize English teaching by enhancing resource quality and offering a more efficient, innovative approach to course development.