CLGT: A Graph Transformer for Student Performance Prediction in Collaborative Learning

Source publication / research team or educational organization described in paper

Not specified in extracted text

Researchers, educators, instructors, or facilitators as described in the source publication

• Research / curriculum design context

Course implementation or course design

Not specified in extracted text

Not specified in extracted text

Explainable AI / robustness, Assessment / tutoring analytics

• Teacher readiness, time, support, and classroom integration may affect implementation quality.
• High-stakes or student-data-centered AI use requires stronger governance, transparency, and bias monitoring.

Unspecified / broad education

Mixed or not explicitly specified; infer from target learner group and intervention design.

Varies by intervention; not specified unless the paper explicitly describes prerequisites.

• Document the AI education intervention, course, tool, or resource described in the source publication.
• Extract the learner context, AI role, pedagogy, outcomes, and constraints for AAB registry comparison.
• Modeling and predicting the performance of students in col- laborative learning paradigms is an important task.

• Support AAB comparison across AI literacy, AI education, teacher training, higher education, and workforce contexts.
• Capture evidence maturity, transferability, and limitations rather than treating the publication as product endorsement.

• Not an AAB endorsement of the tool, curriculum, provider, or result.
• Not a direct replication record unless the source paper reports implementation details sufficient for replication.

Explainable AI / robustness, Assessment / tutoring analytics

Not specified in extracted text

• Evaluator

• Primary interaction pattern inferred from publication: Curriculum / course design, Teacher professional development, Assessment support.
• AI capability focus: Explainable AI / robustness, Assessment / tutoring analytics.

• Minimize personal data collection and avoid storing identifiable learner media unless approved by local policy/IRB.

• Review the publication’s reported context, learner group, AI tool or curriculum, implementation process, and outcome evidence.
• Map the case to AAB registry fields for comparison across educational levels and AI capability types.
• Use the source publication and PDF for any manual verification before public registry release.

• Human educators/researchers remain responsible for instructional design, supervision, interpretation, and ethical safeguards.
• AI systems or AI concepts provide the learning object, support tool, evaluator, simulator, or automation context depending on the paper.

• Instructional / curriculum-based learning
• Registry extraction emphasizes explicit learning goals, observed outcomes, constraints, and safety limitations.

• Teacher readiness, time, support, and classroom integration may affect implementation quality.
• High-stakes or student-data-centered AI use requires stronger governance, transparency, and bias monitoring.

• Case classified under: Published curriculum / implementation paper.
• Pedagogical pattern: Instructional / curriculum-based learning.
• Any additional adaptations should be verified against the full paper before public-facing publication.

• Engagement evidence should be interpreted according to the source paper’s reported method and sample.
• Most of the research presented in literature regarding collaborative learning focuses on the discussion forums and social learning networks.

• Most of the research presented in literature regarding collaborative learning focuses on the discussion forums and social learning networks.

Modeling and predicting the performance of students in col- laborative 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.

• Minimize personal data collection and avoid storing identifiable learner media unless approved by local policy/IRB.

• Learning analytics
• Activity documentation

• Can be used as an AAB evidence record for cross-case comparison, standards drafting, and evidence-maturity mapping.
• Supports identification of recurring patterns in AI literacy, AI education implementation, teacher preparation, assessment, and responsible AI learning.

• Conceptual understanding
• Teacher readiness
• Assessment / feedback quality
• Curriculum / course design
• Teacher professional development
• Assessment support
• Explainable AI / robustness
• Assessment / tutoring analytics

• Completed

Unspecified / broad education

Research / curriculum design context

Explainable AI / robustness, Assessment / tutoring analytics

Instructional / curriculum-based learning

High

High

CLGT: A Graph Transformer for Student Performance Prediction in Collaborative Learning

• Tianhao Peng
• Yu Liang
• Wenjun Wu
• Jian Ren
• Zhao Pengrui
• Yanjun Pu

Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 37 No. 13, EAAI-23

2023

10.1609/aaai.v37i13.26893

https://ojs.aaai.org/index.php/AAAI/article/view/26893

https://ojs.aaai.org/index.php/AAAI/article/view/26893/26665

084_CLGT_ A Graph Transformer for Student Performance Prediction in Collaborative Learning.pdf

8

Modeling and predicting the performance of students in col- laborative 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 soft- ware engineering course are required to team up and complete a software project together. In this work, we construct an in- teraction 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 col- laborative learning (CLGT) for evaluating and predicting the performance of students. Moreover, the proposed CLGT con- tains an interpretation module that explains the prediction re- sults and visualizes the student interaction patterns. The ex- perimental results confirm that the proposed CLGT outper- forms 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.

• Research / curriculum design context

• Teacher readiness, time, support, and classroom integration may affect implementation quality.
• High-stakes or student-data-centered AI use requires stronger governance, transparency, and bias monitoring.

Not specified in extracted text unless noted in duration field.

Requires educators/researchers/facilitators with sufficient AI literacy and pedagogy knowledge for the target learners.

Infrastructure depends on AI tool type, learner devices, data access, and institutional policy context.

High

• group_size
• duration

This entry was automatically extracted from the PDF text and manifest metadata. Fields should be manually verified before public registry publication, especially group size, location, duration, and outcome claims.