Knowledge tracing based on personalized learning and deep refinement

doi:10.11772/j.issn.1001-9081.2023101452

Journal of Computer Applications ›› 2024, Vol. 44 ›› Issue (10): 3039-3046.DOI: 10.11772/j.issn.1001-9081.2023101452

• Artificial intelligence • Previous Articles Next Articles

Knowledge tracing based on personalized learning and deep refinement

Linhao LI¹^,²^,³, Xiaoqian ZHANG¹, Yao DONG¹^,²^,³, Xu WANG¹^,²^,³, Yongfeng DONG¹^,²^,³()

^1.School of Artificial Intelligence，Hebei University of Technology，Tianjin 300401，China
^2.Hebei Province Key Laboratory of Big Data Computing （Hebei University of Technology），Tianjin 300401，China
^3.Hebei Data Driven Industrial Intelligent Engineering Research Center （Hebei University of Technology），Tianjin 300401，China

Received:2023-10-30 Revised:2024-01-20 Accepted:2024-01-26 Online:2024-10-15 Published:2024-10-10
Contact: Yongfeng DONG
About author:LI Linhao， born in 1989， Ph. D.， associate professor. His research interests include machine learning， knowledge inference， computer vision.
ZHANG Xiaoqian， born in 1998， M. S. candidate. Her research interests include machine learning， knowledge tracing.
DONG Yao， born in 1982， Ph. D.， senior experimentalist. Her research interests include graph data mining， learning recommendation.
WANG Xu， born in 1995， Ph. D.， lecturer. His research interests include knowledge graph， Q&A.
Supported by:
Hebei Natural Science Foundation(F2020202028)

基于个性化学习和深层次细化的知识追踪

李林昊¹^,²^,³, 张晓倩¹, 董瑶¹^,²^,³, 王旭¹^,²^,³, 董永峰¹^,²^,³()

^1.河北工业大学人工智能与数据科学学院，天津 300401
^2.河北省大数据计算重点实验室（河北工业大学），天津 300401
^3.河北省数据驱动工业智能工程研究中心（河北工业大学），天津 300401

通讯作者: 董永峰
作者简介:李林昊（1989—），男，山东威海人，副教授，博士，CCF会员，主要研究方向：机器学习、知识推理、计算机视觉
张晓倩（1998—），女，河北石家庄人，硕士研究生，主要研究方向：机器学习、知识追踪
董瑶（1982—），女，河北唐山人，高级实验师，博士，CCF会员，主要研究方向：图数据挖掘、学习推荐
王旭（1995—），男，河北沧州人，讲师，博士，CCF会员，主要研究方向：知识图谱、对话和问答
董永峰（1977—），男，河北定州人，教授，博士，CCF会员，主要研究方向：机器学习、知识工程、计算机视觉、智能信息处理 dongyf@hebut.edu.cn。
基金资助:
河北省自然科学基金资助项目(F2020202028)

Abstract

Abstract:

In response to the problems that Knowledge Tracing （KT） models do not consider differences between students and explore the high matching between knowledge states and exercises， a two-layer network architecture was proposed — Knowledge Tracing based on Personalized Learning and Deep Refinement （PLDRKT）. Firstly， the attention enhancement mechanism was used to obtain a deep refined representation of the exercises. Then， personalized modeling of the initial knowledge state was conducted from the perspectives of different students’ perceptions of difficulty and learning benefits of the exercises. Finally， the initial knowledge states and the deep exercise representations were used to obtain the students’ deep knowledge states and predict their future answering conditions. Comparative experiments were conducted on Statics2011， ASSIST09， ASSIST15， and ASSIST17 datasets among PLDRKT model and seven models such as enhancing Adversarial Training based Knowledge Tracing （ATKT） and ENsemble Knowledge Tracing （ENKT）. Experimental results show that the Area Under the Curve （AUC） of PLDRKT model is increased by 0.61， 1.32， 5.29， and 0.19 percentage points， respectively， compared to the optimal baseline models without considering exercise embedding on four datasets. It can be seen that PLDRKT model can model students’ knowledge states and predict answers effectively.

Key words: Knowledge Tracing (KT), attention, deep refinement, high matching, personalization

摘要：

针对知识追踪（KT）模型没有充分考虑学生间差异、挖掘知识状态与习题的高度匹配等问题，提出一种双层网络架构——基于个性化学习和深层次细化的知识追踪（PLDRKT）。首先，利用增强注意力机制得到习题的深层次细化表示；其次，从不同学生对习题的难度感知和学习收益方面对初步知识状态进行个性化建模；最后，利用初步知识状态和深层习题表示得到学生的深层次知识状态并预测他们的未来答题情况。将PLDRKT模型与基于对抗训练的增强知识追踪（ATKT）和集成知识追踪（ENKT）等7种模型在Statics2011、ASSIST09、ASSIST15和ASSIST17数据集上进行对比实验。实验结果显示，PLDRKT模型的曲线下面积（AUC）均有增加，在4个数据集上与不考虑习题嵌入的最优基线模型相比，分别增加了0.61、1.32、5.29和0.19个百分点，可见PLDRKT模型可以较好地建模学生知识状态并预测回答。

关键词: 知识追踪, 注意力, 深层次细化, 高度匹配, 个性化

CLC Number:

TP391

Linhao LI, Xiaoqian ZHANG, Yao DONG, Xu WANG, Yongfeng DONG. Knowledge tracing based on personalized learning and deep refinement[J]. Journal of Computer Applications, 2024, 44(10): 3039-3046.

李林昊, 张晓倩, 董瑶, 王旭, 董永峰. 基于个性化学习和深层次细化的知识追踪[J]. 《计算机应用》唯一官方网站, 2024, 44(10): 3039-3046.

Figures/Tables 10

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数据集	学生数	知识概念数	习题数	回答交互数
Statics2011	333	1 223	—	189 297
ASSIST09	4 151	110	16 891	325 637
ASSIST15	19 840	100	—	683 801
ASSIST17	1 709	102	3 162	942 816

数据集	学生数	知识概念数	习题数	回答交互数
Statics2011	333	1 223	—	189 297
ASSIST09	4 151	110	16 891	325 637
ASSIST15	19 840	100	—	683 801
ASSIST17	1 709	102	3 162	942 816

模型		AUC
模型		Statics2011	ASSIST09	ASSIST15	ASSIST17
不考虑习题嵌入	DRKT-S	0.832 9±0.005 5	0.824 7±0.002 9	0.822 0±0.004 2	0.729 2±0.004 9
	DRKT-H	0.836 0±0.004 0	0.839 5±0.003 9	0.841 0±0.003 0	0.749 8±0.007 1
	PLDRKT	0.838 6±0.004 5	0.851 8±0.003 5	0.857 4±0.008 7	0.769 1±0.004 4
考虑习题嵌入	DRKT-S^q	—	0.828 9±0.003 6	—	0.782 3±0.000 5
	DRKT-H^q	—	0.846 7±0.003 3	—	0.800 7±0.005 9
	PLDRKT^q	—	0.855 6±0.003 5	—	0.804 7±0.004 9

模型		AUC
模型		Statics2011	ASSIST09	ASSIST15	ASSIST17
不考虑习题嵌入	DRKT-S	0.832 9±0.005 5	0.824 7±0.002 9	0.822 0±0.004 2	0.729 2±0.004 9
	DRKT-H	0.836 0±0.004 0	0.839 5±0.003 9	0.841 0±0.003 0	0.749 8±0.007 1
	PLDRKT	0.838 6±0.004 5	0.851 8±0.003 5	0.857 4±0.008 7	0.769 1±0.004 4
考虑习题嵌入	DRKT-S^q	—	0.828 9±0.003 6	—	0.782 3±0.000 5
	DRKT-H^q	—	0.846 7±0.003 3	—	0.800 7±0.005 9
	PLDRKT^q	—	0.855 6±0.003 5	—	0.804 7±0.004 9

模型		AUC
模型		Statics2011	ASSIST09	ASSIST15	ASSIST17
不考虑习题嵌入	DKT	0.823 3±0.003 9	0.817 0±0.004 3	0.731 0±0.001 8	0.726 3±0.005 4
	DKT+	0.830 1±0.003 9	0.802 4±0.004 5	0.731 3±0.001 8	0.712 4±0.004 1
	DKVMN	0.819 5±0.004 1	0.809 3±0.004 4	0.727 6±0.001 7	0.707 3±0.004 4
	SAKT	0.802 9±0.003 2	0.752 0±0.004 0	0.721 2±0.002 0	0.656 9±0.002 7
	AKT-NR	0.826 5±0.004 9	0.816 9±0.004 5	0.782 8±0.001 9	0.728 2±0.003 7
	ATKT	0.832 5±0.004 3	0.824 4±0.003 2	0.804 5±0.009 7	0.729 7±0.005 1
	ENKT^c	0.831 4±0.005 4	0.838 6±0.004 1	0.732 8±0.002 1	0.767 2±0.003 1
	PLDRKT	0.838 6±0.004 5	0.851 8±0.003 5	0.857 4±0.008 7	0.769 1±0.004 4
考虑习题嵌入	AKT-R	—	0.834 6±0.003 6	—	0.770 2±0.002 6
	ENKT^q	—	0.839 4±0.004 5	—	0.774 4±0.003 3
	PLDRKT^q	—	0.855 6±0.003 5	—	0.804 7±0.004 9

Knowledge tracing based on personalized learning and deep refinement

基于个性化学习和深层次细化的知识追踪

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