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

Αποθηκεύτηκε σε:

Λεπτομέρειες βιβλιογραφικής εγγραφής
Εκδόθηκε σε:	International Journal of Web-Based Learning and Teaching Technologies vol. 20, no. 1 (2025), p. 1-23
Κύριος συγγραφέας:	Jiang, Qiujie
Έκδοση:	IGI Global
Θέματα:	Individual differences Teachers Feedback Student-centered learning Teaching College students Distance learning Customization Students Colleges & universities Teaching methods Intelligence Academic achievement Neural networks Learning Artificial intelligence Computer assisted instruction > CAI Ability Satisfaction Educational Technology College English Teaching Models Algorithms
Διαθέσιμο Online:	Citation/Abstract Full Text - PDF
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Περιγραφή
Περίληψη:	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.
ISSN:	1548-1093 1548-1107
DOI:	10.4018/IJWLTT.386764
Πηγή:	Applied Social Sciences Index & Abstracts (ASSIA)