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RETRACTED: TGEL-transformer: Fusing educational theories with deep learning for interpretable student performance
Yuhao Gong1, Fei Wang2, Yuchen Zhang3
1Nanchang Hangkong University, Nanchang, Jiangxi, China.
Plos One
|June 30, 2025
Summary
The new Theory-Guided Educational Learning Transformer (TGEL-Transformer) framework enhances personalized learning by integrating educational theories with AI. It significantly improves learning outcome prediction and identifies key influencing factors like teacher support and peer interaction.
Area of Science:
- Educational Technology
- Artificial Intelligence
- Data Mining
Background:
- Traditional educational data mining struggles with heterogeneous data and complex interactions.
- Existing deep learning models lack educational theory guidance and interpretability.
Purpose of the Study:
- To propose the TGEL-Transformer framework for theory-guided personalized learning.
- To address limitations in integrating diverse features and interpreting learning dynamics.
Main Methods:
- Developed a dual-channel feature processing module for cognitive, affective, and environmental data.
- Implemented a theory-guided four-head attention mechanism to model educational interactions.
- Utilized an interpretable prediction layer for theoretical support in interventions.
Main Results:
- TGEL-Transformer achieved RMSE = 1.87 and R2 = 0.75 on a dataset of 6,608 students.
- Demonstrated statistically significant improvements over existing methods (p < 0.001).
- External validation on cross-cultural data (n = 480) showed strong generalizability (R2 = 0.683).
Conclusions:
- TGEL-Transformer offers a theory-guided approach to educational data mining.
- Identified teacher support, prior knowledge, and peer interaction as key factors for personalized education.
- Provides data-driven support for advancing intelligent education development.
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