计算机工程与应用2026，Vol.62Issue(12)：37-59,23.DOI:10.3778/j.issn.1002-8331.2508-0006
基于图神经网络的深度知识追踪方法综述
Survey on Deep Knowledge Tracing Methods Based on Graph Neural Networks
摘要
Abstract
With the advancement of digital transformation in education,deep knowledge tracing has emerged as a research focus in intelligent education due to its ability to dynamically model learners'knowledge states and support personalized learning through advanced algorithms.However,traditional deep learning-based models often struggle to capture the com-plex relationships between learners and knowledge concepts.Deep knowledge tracing based on graph neural networks address this limitation by leveraging graph structures to model these intricate interactions more effectively,leading to improved prediction accuracy and interpretability.This paper systematically reviews recent research on deep knowledge tracing based on graph neural networks.It introduces the concepts of knowledge tracing and graph neural networks.Then,it summarizes the deep knowledge tracking methods based on graph neural networks,graph convolutional networks,graph attention networks,and graph memory networks,and categorizes existing methods into three perspectives:using dif-ferent graph structures,incorporating additional features,and integrating educational psychology factors.In addition,the paper reviews five benchmark datasets commonly used in deep knowledge tracing and presents performance evaluations of representative models on these datasets.Finally,it analyzes the existing challenges and open issues in this research domain.关键词
图神经网络/深度知识追踪/个性化学习/深度学习/图结构Key words
graph neural networks/deep knowledge tracing/personalized learning/deep learning/graph structure分类
信息技术与安全科学引用本文复制引用
周楚雄,张丽萍,闫盛,李娜,王东奇..基于图神经网络的深度知识追踪方法综述[J].计算机工程与应用,2026,62(12):37-59,23.基金项目
国家自然科学基金(61462071) （61462071）
内蒙古自然科学基金(2023LHMS06009,2024MS06020,2025MS06055) （2023LHMS06009,2024MS06020,2025MS06055）
内蒙古自治区教育科学研究"十四五"规划2023年度课题(2023NGHZXZH119,NGJGH2023234) （2023NGHZXZH119,NGJGH2023234）
内蒙古师范大学基本科研业务费专项资金项目(2022JBQN108,2022JBQN008). （2022JBQN108,2022JBQN008）
