GOAT: a novel global-local optimized graph transformer framework for predicting student performance in collaborative learning
Abstract 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 featur...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-03-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-025-93052-y |
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| _version_ | 1850057601318912000 |
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| author | Tianhao Peng Qiang Yue Yu Liang Jian Ren Jie Luo Haitao Yuan Wenjun Wu |
| author_facet | Tianhao Peng Qiang Yue Yu Liang Jian Ren Jie Luo Haitao Yuan Wenjun Wu |
| author_sort | Tianhao Peng |
| collection | DOAJ |
| description | Abstract 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. |
| format | Article |
| id | doaj-art-e1167770fa6d44d8b30cc8fba201dfcd |
| institution | DOAJ |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-e1167770fa6d44d8b30cc8fba201dfcd2025-08-20T02:51:23ZengNature PortfolioScientific Reports2045-23222025-03-0115111610.1038/s41598-025-93052-yGOAT: a novel global-local optimized graph transformer framework for predicting student performance in collaborative learningTianhao Peng0Qiang Yue1Yu Liang2Jian Ren3Jie Luo4Haitao Yuan5Wenjun Wu6Beihang UniversityBeihang UniversityBeijing University of TechnologyBeihang UniversityBeihang UniversityNanyang Technological UniversityBeihang UniversityAbstract 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.https://doi.org/10.1038/s41598-025-93052-y |
| spellingShingle | Tianhao Peng Qiang Yue Yu Liang Jian Ren Jie Luo Haitao Yuan Wenjun Wu GOAT: a novel global-local optimized graph transformer framework for predicting student performance in collaborative learning Scientific Reports |
| title | GOAT: a novel global-local optimized graph transformer framework for predicting student performance in collaborative learning |
| title_full | GOAT: a novel global-local optimized graph transformer framework for predicting student performance in collaborative learning |
| title_fullStr | GOAT: a novel global-local optimized graph transformer framework for predicting student performance in collaborative learning |
| title_full_unstemmed | GOAT: a novel global-local optimized graph transformer framework for predicting student performance in collaborative learning |
| title_short | GOAT: a novel global-local optimized graph transformer framework for predicting student performance in collaborative learning |
| title_sort | goat a novel global local optimized graph transformer framework for predicting student performance in collaborative learning |
| url | https://doi.org/10.1038/s41598-025-93052-y |
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