知识追踪旨在评估学习者的学习状态，并根据先前的答题情况预测他们未来的答题表现.然而现有的知识追踪模型大多仅关注问题或技能间的关联，忽略了学生与问题间的结构关系.为此我们提出了基于学生-问题关联的异构图知识追踪模型（Student-Problem association based heterogeneous graph Knowledge Tracing model
Wu Zhengyang,Huang Li,Huang Qionghao,Huang Changqin,Tang Yong.SGKT: Session graph-based knowledge tracing for student performance prediction[J].Expert Systems With Applications,2022.
liu qi,Huang Zhenya,Yin Yu,Chen Enhong,Xiong Hui,Su Yu,Hu Guoping.EKT: Exercise-aware Knowledge Tracing for Student Performance Prediction[J].IEEE Transactions on Knowledge and Data Engineering,2019.
Chris Piech,Jonathan Spencer,Jonathan Huang,Surya Ganguli,Mehran Sahami,Leonidas J. Guibas,Jascha Sohl-Dickstein.Deep Knowledge Tracing.[J].CoRR,2015.
Albert T. Corbett,John R. Anderson.Knowledge tracing: Modeling the acquisition of procedural knowledge[J].User Modeling and User-Adapted Interaction,1995(4).
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