Innovation of Multimodal Learning Paths Based on Learning Behavior and Sentiment Analysis in AI Digital Intelligence Platform

Abstract

This study develops an intelligent multimodal learning path recommendation system to address the problems of lack of personalized learning paths and insufficient attention to students’ emotional impact in traditional digital education models. By integrating learning behavior analysis and sentiment analysis, the Random Forest (RF) model is used to deeply mine students’ learning behavior data, such as analyzing learning time, resource access frequency, etc., to precisely understand students’ learning patterns. At the same time, with the help of sentiment analysis technology based on BERT (Bidirectional Encoder Representations from Transformers), students’ emotional states during the learning process are monitored in real-time, including positive, negative, neutral, and other emotions. The experimental results show that by dynamically adjusting the learning path and monitoring students’ emotional states, students’ average scores increase by 6.0%, and homework completion rate increases by 4.4%. This study not only improves the overall quality of education, but also provides educators with more scientific decision-making support tools.