CLGT: A Graph Transformer for Student Performance Prediction in Collaborative Learning

TL;DR

This paper introduces CLGT, a graph transformer model that predicts student performance in collaborative projects by analyzing interaction graphs, providing explanations and early warnings to improve educational outcomes.

Contribution

The paper presents a novel graph transformer framework for collaborative learning that incorporates interaction analysis and interpretability, outperforming baseline models in real-world student performance prediction.

Findings

CLGT outperforms baseline models in accuracy.

It effectively identifies students at risk of poor performance.

The model provides interpretable insights into student interactions.

Abstract

Modeling and predicting the performance of students in collaborative learning paradigms is an important task. Most of the research presented in literature regarding collaborative learning focuses on the discussion forums and social learning networks. There are only a few works that investigate how students interact with each other in team projects and how such interactions affect their academic performance. In order to bridge this gap, we choose a software engineering course as the study subject. The students who participate in a software engineering course are required to team up and complete a software project together. In this work, we construct an interaction graph based on the activities of students grouped in various teams. Based on this student interaction graph, we present an extended graph transformer framework for collaborative learning (CLGT) for evaluating and predicting the performance of students. Moreover, the proposed CLGT contains an interpretation module that explains the prediction results and visualizes the student interaction patterns. The experimental results confirm that the proposed CLGT outperforms the baseline models in terms of performing predictions based on the real-world datasets. Moreover, the proposed CLGT differentiates the students with poor performance in the collaborative learning paradigm and gives teachers early warnings, so that appropriate assistance can be provided.