A Deep Learning Multimodal Feature Fusion Approach for Assessing English Study

With the advances in big data and AI technology, machine learning (ML) and deep learning (DL) have become popular in research and application. ML uses data analysis to recognize patterns and make predictions, while DL uses artificial neural networks to learn complex data representations and solve complicated problems. In the field of education, AI methods are gradually being applied. This article proposes a method based on the fusion of multimodal features to evaluate student learning in an English course. This method extracts and fuses weighted features from the three modalities of students' head movement, participation in discussions, and correctness of exercises. This practical approach can improve teaching strategies and the quality of instruction while providing a more comprehensive understanding of student learning.