[1]张凯,刘月,覃正楚,等.迁移表征的知识追踪模型[J].智能系统学报,2024,19(4):974-982.[doi:10.11992/tis.202302002]
ZHANG Kai,LIU Yue,QIN Zhengchu,et al.Knowledge tracing model via exercise transfer representation[J].CAAI Transactions on Intelligent Systems,2024,19(4):974-982.[doi:10.11992/tis.202302002]
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ZHANG Kai,LIU Yue,QIN Zhengchu,et al.Knowledge tracing model via exercise transfer representation[J].CAAI Transactions on Intelligent Systems,2024,19(4):974-982.[doi:10.11992/tis.202302002]
迁移表征的知识追踪模型
《智能系统学报》[ISSN 1673-4785/CN 23-1538/TP] 卷:
19
期数:
2024年第4期
页码:
974-982
栏目:
学术论文—知识工程
出版日期:
2024-07-05
- Title:
- Knowledge tracing model via exercise transfer representation
- Keywords:
- knowledge tracing; learning transfer mechanism; exercise representation; exercise transfer; sequence model; answer prediction; attention mechanism; threshold mechanism
- 分类号:
- TP183
- 摘要:
- 针对多数知识追踪研究在表征题目时仅利用了题目包含的概念等显性特征，未能考虑到题目中概念的考察侧重程度这一隐性特征，也未表征迁移过程中题目的迁移程度的问题，本文提出题目迁移表征的知识追踪模型。在题目侧重表征方面，采用加性注意力机制提取题目中各个概念的考察侧重程度；在题目迁移方面，利用相似性和通道注意力机制融合建模历史题目多角度的迁移程度；在迁移遗忘方面，使用门限机制建模学习迁移的遗忘过程。最终得到题目迁移表征，以此来预测学习者未来的答题表现。在实验阶段，与6种相关模型在3个真实数据集上进行对比实验，结果表明提出模型的曲线下面积(area under the curve, AUC)和准确率(accuracy, ACC)均有更好表现，尤其在ASSISTments2012数据集上表现最佳，相较于其他对比模型分别提升了3.5%~20.1%和2.3%~18.5%；在可解释性方面，使用图表可视化描述了题目迁移表征生成路径。本研究建模的学习迁移内在机制可为知识追踪模型的设计提供参考。
- Abstract:
- In most knowledge tracing research, the representation of questions typically relies solely on explicit features such as the concepts contained within the questions, neglecting the implicit feature of the emphasis level on concepts and failing to characterize the degree of question transfer during the transfer process. This paper presents a knowledge tracing model via exercise transfer representation. In terms of the representation of the focus of the exercise, the additive attention mechanism is used to extract the focus of each concept in the exercise. In terms of exercise transfer, the multi-angle transfer degree of historical exercises is modeled by fusion of similarity and channel attention mechanism. In terms of transfer forgetting, a threshold mechanism is used to model the forgetting process of learning transfer. Finally, the exercise transfer representation is obtained to predict the learner’s future answering performance. Experimental results show that the proposed model outperforms six benchmark models across three real datasets, particularly excelling on the ASSISTments 2012 dataset with improvements ranging from 3.5% to 20.1% for area under the curve (AUC) and 2.3% to 18.5% for accuracy (ACC). The interpretability aspect is enhanced through graphical visualization of the question transfer representation generation pathway. The internal mechanisms of learning transfer modeled in this paper provide valuable insights for the design of knowledge tracing models.
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- 相似文献/References:
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[1]陈恩红,刘淇,王士进,等.面向智能教育的自适应学习关键技术与应用[J].智能系统学报,2021,16(5):886.[doi:10.11992/tis.202105036]
CHEN Enhong,LIU Qi,WANG Shijin,et al.Key techniques and application of intelligent education oriented adaptive learning[J].CAAI Transactions on Intelligent Systems,2021,16():886.[doi:10.11992/tis.202105036]
备注/Memo
收稿日期:2023-02-02。
基金项目:国家自然科学基金项目（62077018）；国家科技部高端外国人才引进计划项目（G2022027006L）；湖北省自然科学基金项目（2022CFB132）：湖北省教育厅科学研究计划项目（B2022038）；2023年湖北本科高校省级教学改革研究项目(2023273).
作者简介:张凯，教授，博士，主要研究方向为图神经网络、贝叶斯深度学习、知识追踪、知识图谱，湖北省科技厅入库专家。主持或参与完成国家、省部级科研项目15项，发表学术论文20余篇。E-mail：kai.zhang@yangtzeu.edu.cn;刘月，硕士研究生，主要研究方向为深度学习、知识追踪。E-mail：2021710577@yangtzeu.edu.cn;覃正楚，硕士研究生，主要研究方向为深度学习、知识追踪。E-mail：2021710632@yangtzeu.edu.cn
通讯作者:张凯. E-mail：kai.zhang@yangtzeu.edu.cn
更新日期/Last Update:
1900-01-01