Work

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

Year: 2025

Type: conference-paper

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.

Source: Procedia Computer Science

DOI: 10.1016/j.procs.2025.04.246

Authors: Lei Wang, Nan Peng, Lu Liu, Sheng Wei

Institutions: East University Of Heilongjiang, Harbin University of Commerce

Language: English

Topic: Online Learning and Analytics

Subfield: Computer Science Applications

Field: Computer Science

Domain: Physical Sciences

SDG: Quality education

Open Access status: diamond

Funder: Ministry of Education