Application of Multimodal Attention Reconstruction Method in College English Learning Path Evaluation

Abstract

The purpose of this study is to develop and evaluate an intelligent personalized learning path generation system for college English, so as to cope with the limitations of the traditional teaching model in meeting the individual differences of students. By integrating neural network algorithm, user model, and knowledge map, the system can design customized learning path according to each student's specific situation, track learning progress in real time, and provide timely feedback. The research not only emphasizes the “student-centered” educational concept but also uses advanced calculation models to deeply analyze students' learning behavior and performance data so as to achieve accurate teaching guidance. The experimental results show that compared with traditional methods, personalized learning path based on intelligent technology significantly improves students' learning effect and satisfaction, and also enhances teachers' ability to support individualized teaching.