基于学生⁃问题关联的异构图知识追踪模型

闫秋艳; 司雨晴; 袁冠; 王志晓

doi:10.12263/DZXB.20221427

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基于学生⁃问题关联的异构图知识追踪模型

学术论文 | 更新时间：2025-12-11

- 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
- 电子学报 2023年51卷第12期页码：3549-3556
- 作者机构：
  
  中国矿业大学计算机科学与技术学院，江苏徐州 221116
- 作者简介：
  
  [ "闫秋艳女，1978年8月出生于江苏省徐州市.现为中国矿业大学计算机科学与技术学院副教授、博士生导师.研究方向为教育大数据挖掘、机器学习.E-mail: yanqy@cumt.edu.cn" ]
  [ "司雨晴女，1997年8月出生于江苏省徐州市.本科毕业于中国矿业大学信息安全专业，硕士就读于中国矿业大学计算机应用技术专业.E-mail: siyq@cumt.edu.cn" ]
  [ "袁冠（通讯作者）男，1982年生，江苏睢宁人.现任中国矿业大学计算机科学与技术学院教授.主要研究方向包括时空大数据技术以及计算智能.E-mail: yuanguan@cumt.edu.cn" ]
- 基金信息：
  
  国家自然科学基金(62277046;61977061)
- DOI：10.12263/DZXB.20221427
  中图分类号： TP301;
- 收稿：2022-12-21，
  
  修回：2023-08-22，
  
  纸质出版：2023-12-25
- 稿件说明：
移动端阅览
闫秋艳,司雨晴,袁冠等.基于学生⁃问题关联的异构图知识追踪模型[J].电子学报,2023,51(12):3549-3556.

YAN Qiu-yan,SI Yu-qing,YUAN Guan,et al.Student-Problem Association Based Heterogeneous Graph Knowledge Tracing Model[J].ACTA ELECTRONICA SINICA,2023,51(12):3549-3556.
闫秋艳,司雨晴,袁冠等.基于学生⁃问题关联的异构图知识追踪模型[J].电子学报,2023,51(12):3549-3556. DOI： 10.12263/DZXB.20221427.

YAN Qiu-yan,SI Yu-qing,YUAN Guan,et al.Student-Problem Association Based Heterogeneous Graph Knowledge Tracing Model[J].ACTA ELECTRONICA SINICA,2023,51(12):3549-3556. DOI： 10.12263/DZXB.20221427.

摘要

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，SPKT）.该模型在知识追踪中融合了学生的学习能力和问题的重要性，并使用图注意力网络学习学生问题间的关联，获得学生、问题的嵌入表示并进行知识状态的预测.通过在真实公开数据集上的性能对比和模型消融实验，并可视化SPKT模型的知识追踪效果，证明了SPKT在预测性能上优于现有的知识追踪模型.

Abstract

Knowledge tracing aims to assess learners' learning status and predict their future performance based on previous answers. However

most of the existing knowledge tracing models only focus on the relationship between problems or skills

and ignore the structural relationship between students and problems. Therefore

we propose a student‑problem association based heterogeneous graph knowledge tracing model (SPKT) . The model integrates the learning ability of students and the importance of problems in knowledge tracing

and uses graph attention network to learn the interaction between students' problems

so as to obtain the embedded representation of students and problems

meanwhile predicting the learners' status. It is proved that SPKT is superior to the existing knowledge tracking model in terms of prediction performance through a large number of experiments and data visualization.

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references

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