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GOAT: a novel global-local optimized graph transformer framework for predicting student performance in collaborative

Tianhao Peng^1,2, Qiang Yue^1,2, Yu Liang³

¹Beihang University, Beijing, 100191, China.

Scientific Reports

|March 22, 2025

Summary

This summary is machine-generated.

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.

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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.