Learning behavior boosted knowledge tracing model

doi:10.11772/j.issn.1001-9081.2024081153

Journal of Computer Applications ›› 2025, Vol. 45 ›› Issue (9): 2747-2754.DOI: 10.11772/j.issn.1001-9081.2024081153

• Artificial intelligence • Previous Articles

Learning behavior boosted knowledge tracing model

Wei ZHANG, Zhongwei GONG(), Zhixin LI, Peihua LUO, Lingling SONG

Faculty of Artificial Intelligence Education，Central China Normal University，Wuhan Hubei 430079，China

Received:2024-08-16 Revised:2025-01-06 Accepted:2025-01-10 Online:2025-02-17 Published:2025-09-10
Contact: Zhongwei GONG
About author:ZHANG Wei， born in 1975， Ph. D.， professor. His research interests include big data analysis， data mining， intelligent education evaluation.
LI Zhixin， born in 2000， M. S. candidate. His research interests include knowledge tracing.
LUO Peihua， born in 1999， M. S. candidate. Her research interests include knowledge tracing.
SONG Lingling， born in 1994， Ph. D. candidate. Her research interests include knowledge tracing， cognitive diagnosis.
Supported by:
National Natural Science Foundation of China(62377024)

学习行为增强的知识追踪模型

张维, 龚中伟(), 李志新, 罗佩华, 宋玲玲

华中师范大学人工智能教育学部，武汉 430079

通讯作者: 龚中伟
作者简介:张维（1975—），男，湖北仙桃人，教授，博士，主要研究方向：大数据分析、数据挖掘、智能教育评价
李志新（2000—），男，湖北潜江人，硕士研究生，主要研究方向：知识追踪
罗佩华（1999—），女，广东兴宁人，硕士研究生，主要研究方向：知识追踪
宋玲玲（1994—），女，湖北天门人，博士研究生，主要研究方向：知识追踪、认知诊断。
第一联系人：
基金资助:
国家自然科学基金资助项目(62377024)

Abstract

Abstract:

The existing Knowledge Tracing （KT） models fail to effectively utilize information about learning behaviors and ignore the differences in contributions of different learning behaviors to question-answering performance. For this reason， a Learning Behavior Boosted Knowledge Tracing （LBBKT） model was proposed. In this model， Gated Residual Network （GRN） was used to encode students’ learning behavior features into four context vectors and embed them into the model， thereby making full use of the learning behavior information （speed of question-answering， number of attempts， and prompts） to better model students’ learning processes. In addition， the students’ learning behavior features were weighted selectively by using variable selection network， and the interference of irrelevant features was suppressed through GRN， so as to enhance the influence of relevant features on students’ question-answering performance， thereby fully considering differential contributions of different learning behaviors to students’ question-answering performance. Experimental results on several public datasets show that LBBKT model outperforms the comparative KT models significantly in terms of prediction accuracy.

Key words: Knowledge Tracing (KT), learning behavior, variable selection network, selective weighting, Gated Residual Network (GRN)

摘要：

现有的知识追踪（KT）模型未能有效利用学习行为信息，且忽略了不同学习行为对答题表现的贡献差异。因此，提出一种学习行为增强的知识追踪（LBBKT）模型。该模型采用门控残差网络（GRN）将学生的学习行为特征编码成4种上下文向量并把它们融入模型中，从而充分利用学习行为信息（答题速度、尝试次数和提示）更好地建模学生的学习过程。此外，利用变量选择网络对学生的学习行为特征进行选择性加权，并通过GRN抑制不相关特征的干扰，以增强相关特征对学生答题表现的影响，从而充分考虑不同学习行为对学生答题表现的差异性贡献。在多个公开数据集上的实验结果表明，LBBKT模型在预测准确性上显著优于对比的KT模型。

关键词: 知识追踪, 学习行为, 变量选择网络, 选择性加权, 门控残差网络

CLC Number:

TP391

Wei ZHANG, Zhongwei GONG, Zhixin LI, Peihua LUO, Lingling SONG. Learning behavior boosted knowledge tracing model[J]. Journal of Computer Applications, 2025, 45(9): 2747-2754.

张维, 龚中伟, 李志新, 罗佩华, 宋玲玲. 学习行为增强的知识追踪模型[J]. 《计算机应用》唯一官方网站, 2025, 45(9): 2747-2754.

Figures/Tables 7

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数据集	学生数	问题数	概念数	交互数
ASSIST2009	4 151	16 891	110	325 637
ASSIST2012	28 834	53 091	198	2 630 080
ASSISTChall	1 709	102	3 162	942 816

数据集	学生数	问题数	概念数	交互数
ASSIST2009	4 151	16 891	110	325 637
ASSIST2012	28 834	53 091	198	2 630 080
ASSISTChall	1 709	102	3 162	942 816

模型	ASSIST2009		ASSIST2012		ASSISTChall
模型	AUC	ACC	AUC	ACC	AUC	ACC
DKT	70.6	69.0	71.7	72.5	72.3	71.4
DKVMN	72.2	71.1	71.9	72.9	73.2	71.2
SAKT	78.6	76.9	72.4	73.2	72.6	71.4
AKT	82.9	77.6	77.1	75.2	77.2	72.4
LPKT	76.0	72.9	79.4	73.4	79.9	74.1
LPKT-S	78.1	73.6	79.8	74.2	80.7	74.5
LBBKT	83.8	84.1	82.7	77.9	85.1	75.2

模型	ASSIST2009		ASSIST2012		ASSISTChall
模型	AUC	ACC	AUC	ACC	AUC	ACC
DKT	70.6	69.0	71.7	72.5	72.3	71.4
DKVMN	72.2	71.1	71.9	72.9	73.2	71.2
SAKT	78.6	76.9	72.4	73.2	72.6	71.4
AKT	82.9	77.6	77.1	75.2	77.2	72.4
LPKT	76.0	72.9	79.4	73.4	79.9	74.1
LPKT-S	78.1	73.6	79.8	74.2	80.7	74.5
LBBKT	83.8	84.1	82.7	77.9	85.1	75.2

模型	ASSIST2009		ASSISTChall
模型	AUC	ACC	AUC	ACC
LBBKT-NB	82.7	83.4	84.4	74.2
LBBKT-NV	83.1	83.5	83.7	73.6
LBBKT-NG	82.6	83.1	83.9	73.8
LBBKT	83.8	84.1	85.1	75.2

Learning behavior boosted knowledge tracing model

学习行为增强的知识追踪模型

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