  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.
