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

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.