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.

翻译：建模与预测学生在协作学习模式中的表现是一项重要任务。现有文献中关于协作学习的研究大多聚焦于讨论论坛和社会学习网络，仅有少量工作探讨学生在团队项目中如何互动以及这种互动如何影响其学业成绩。为填补这一研究空白，我们选取一门软件工程课程作为研究对象。参与该课程的学生需组队并共同完成一个软件项目。本研究基于不同团队内学生的活动构建交互图，并提出一种面向协作学习的扩展图Transformer框架（CLGT），用于评估和预测学生成绩。此外，所提CLGT包含一个解释模块，可阐述预测结果并可视化学生交互模式。实验结果表明，基于真实世界数据集，所提CLGT在预测性能上优于基线模型。同时，CLGT能有效识别协作学习模式中表现不佳的学生，为教师提供早期预警，以便及时提供针对性帮助。