Peng Tianhao, Yue Qiang, Liang Yu, Ren Jian, Luo Jie, Yuan Haitao, Wu Wenjun
Beihang University, Beijing, 100191, China.
State Key Laboratory of Complex & Critical Software Environment, Beijing, 100191, China.
Sci Rep. 2025 Mar 21;15(1):9861. doi: 10.1038/s41598-025-93052-y.
Collaborative learning is a prevalent learning method, and modeling and predicting student performance in such paradigms is an important task. Most current methods analyze this complex task solely based on the frequency of student activities, overlooking the rich spatial and temporal features present in these activities, as well as the diverse textual content provided by various learning artifacts. To address these challenges, we choose a software engineering course as the study subject, where students are required to team up and complete a software project together. In this paper, we propose a novel Global-local Optimized grAph Transformer framework for collaborative learning, termed GOAT. Specifically, we first construct the dynamic knowledge concept-enhanced interaction graphs with nodes representing both students and relevant software engineering concepts, and edges illustrating interactions. Additionally, we incorporate spatial-aware and temporal-aware modules to capture the respective information, enabling the modeling of dynamic interactions within and across learning teams over time. A global-local optimization module is introduced to model intricate relationships within and between teams, highlighting commonalities and differences among team members. Our framework is backed by theoretical analysis and validated through extensive experiments on real-world datasets, which demonstrate its superiority over existing methods.
协作学习是一种普遍的学习方法，在此类范式中对学生表现进行建模和预测是一项重要任务。当前大多数方法仅基于学生活动的频率来分析这一复杂任务，忽略了这些活动中存在的丰富时空特征以及各种学习工件提供的多样文本内容。为应对这些挑战，我们选择一门软件工程课程作为研究对象，学生需组队共同完成一个软件项目。在本文中，我们提出了一种用于协作学习的新颖的全局-局部优化图Transformer框架，称为GOAT。具体而言，我们首先构建动态知识概念增强交互图，其节点代表学生和相关软件工程概念，边表示交互。此外，我们纳入空间感知和时间感知模块以捕获各自的信息，从而能够对学习团队内部和跨团队随时间的动态交互进行建模。引入一个全局-局部优化模块来对团队内部和团队之间的复杂关系进行建模，突出团队成员之间的共性和差异。我们的框架得到了理论分析的支持，并通过在真实世界数据集上的大量实验进行了验证，这些实验证明了它相对于现有方法的优越性。
