Multimodal Learning Analytics-Driven Personalized Development and Guidance Strategies for University Students

DOI

10.2991/978-2-38476-523-2_6How to use a DOI?

Keywords

Multimodal Learning Analytics; Personalized Learning; Educational Data Mining; Student Performance Prediction; Adaptive Learning Systems

Abstract

This study proposes a comprehensive multimodal learning analytics (MMLA) framework that integrates behavioral interactions, physiological signals, emotional expressions, and academic performance to provide personalized development guidance for university students. We designed an adaptive system combining CNN, BiLSTM, and attention mechanisms to process multimodal educational data. A longitudinal study involving 412 undergraduate students across three universities over one semester demonstrated that our approach achieved 87.3% accuracy in predicting student performance and 84.6% accuracy in identifying at-risk students, significantly outperforming traditional single-modal analytics by 15.8% and 12.4% respectively. The personalized guidance strategies led to 23.7% improvement in student engagement and 18.5% increase in academic performance compared to conventional methods. This research demonstrates the effectiveness of multimodal learning analytics in creating adaptive educational environments that respond to individual student needs in real-time.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Jiemei Wang
AU  - Qiaodan Gan
PY  - 2025
DA  - 2025/12/29
TI  - Multimodal Learning Analytics-Driven Personalized Development and Guidance Strategies for University Students
BT  - Proceedings of the 5th International Conference on New Media Development and Modernised Education (NMDME 2025)
PB  - Atlantis Press
SP  - 44
EP  - 50
SN  - 2352-5398
UR  - https://doi.org/10.2991/978-2-38476-523-2_6
DO  - 10.2991/978-2-38476-523-2_6
ID  - Wang2025
ER  -