{"type":"ScholarlyArticle","title":"Relation-Aware Graph Learning with Mixture-of-Experts Prediction for Cognitive Diagnosis","authors":[{"name":"Jingwei Qu","slug":"jingwei-qu","given":"Jingwei","family":"Qu","affiliation":""},{"name":"Mingze Zhang","slug":"mingze-zhang","given":"Mingze","family":"Zhang","affiliation":""},{"name":"Pingshun Zhang","slug":"pingshun-zhang","given":"Pingshun","family":"Zhang","affiliation":""},{"name":"Li Tao","slug":"li-tao","given":"Li","family":"Tao","affiliation":""},{"name":"Ying Wang","slug":"ying-wang","given":"Ying","family":"Wang","affiliation":""},{"name":"Zhaofang Yang","slug":"zhaofang-yang","given":"Zhaofang","family":"Yang","affiliation":""},{"name":"Haibin Ling","slug":"haibin-ling","given":"Haibin","family":"Ling","affiliation":""}],"abstract":"Cognitive diagnosis aims to infer students’ concept-level mastery from exercise response logs and exercise-concept associations. Fully leveraging heterogeneous relations and modeling large mastery-difficulty variations remain challenging, especially with a single predictor. To address these challenges, we propose RMCD, a unified cognitive diagnosis model that integrates relation-aware graph learning with Mixture-of-Experts (MoE) prediction. RMCD constructs a heterogeneous relational graph over students, exercises, and concepts with multiple relation types, and learns node and edge representations simultaneously. It derives relation-strength vectors from student-concept and exercise-concept edges to distinguish relation effects and refine node representations. RMCD further introduces an MoE-based prediction head that adaptively combines multiple expert predictors to capture diverse mastery-difficulty discrepancies. Experiments on benchmark datasets demonstrate that RMCD consistently outperforms state-of-the-art cognitive diagnosis methods. Our algorithm is available at https://github.com/swu-qjw-lab/code/tree/main/RMCD.","doi":"10.24963/ijcai.2026/333","url":"https://ijcai.org/proceedings/2026/333","pdf":"https://ijcai.org/proceedings/2026/0333.pdf","markdown":"https://ijcai.org/proceedings/2026/333.md","bibtex":"https://ijcai.org/proceedings/2026/bibtex/333","volume":{"slug":"2026","title":"Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2026","url":"https://ijcai.org/proceedings/2026/","year":2026,"location":"Bremen, Germany","dates":"15-21 August 2026","editors":"Diego Calvanese","isbn":"978-1-956792-09-6","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2026"},"section":"Data Mining","track":"Main Track","number":333,"pages":{"first":2997,"last":3005},"keywords":["Multidisciplinary Topics and Applications: Education","Data Mining: Mining graphs"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}