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GOAT: a novel global-local optimized graph transformer framework for predicting student performance in collaborative
Tianhao Peng1,2, Qiang Yue1,2, Yu Liang3
1Beihang University, Beijing, 100191, China.
Scientific Reports
|March 22, 2025
Summary
This study introduces GOAT, a novel framework for predicting student performance in collaborative learning by analyzing dynamic interactions and textual content. GOAT enhances collaborative learning analytics by capturing spatial, temporal, and global-local team dynamics.
Area of Science:
- Educational Technology
- Computer Science
- Software Engineering Education
Background:
- Collaborative learning is prevalent, but predicting student performance remains challenging.
- Current methods often overlook spatial, temporal, and textual data in collaborative activities.
- Software engineering projects offer a rich environment for studying team dynamics.
Purpose of the Study:
- To propose a novel framework, GOAT, for enhanced student performance modeling in collaborative learning.
- To incorporate spatial, temporal, and textual features often missed by existing methods.
- To improve the accuracy of predicting student performance in software engineering team projects.
Main Methods:
- Developed the Global-local Optimized grAph Transformer (GOAT) framework.
- Constructed dynamic knowledge concept-enhanced interaction graphs.
- Incorporated spatial-aware and temporal-aware modules for dynamic interaction modeling.
- Utilized a global-local optimization module to analyze intra- and inter-team relationships.
Main Results:
- GOAT effectively models dynamic interactions within and across learning teams over time.
- The framework captures complex relationships, highlighting team member commonalities and differences.
- Experimental validation on real-world datasets demonstrates GOAT's superiority over existing methods.
Conclusions:
- The proposed GOAT framework offers a significant advancement in modeling and predicting student performance in collaborative software engineering projects.
- Integrating diverse data features (spatial, temporal, textual) leads to more accurate performance predictions.
- GOAT provides a robust approach for analyzing complex collaborative learning dynamics.
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