A deeper knowledge tracking model integrating cognitive theory and learning behavior

doi:10.3233/JIFS-235723

IR > 计算机与通信学院

	A deeper knowledge tracking model integrating cognitive theory and learning behavior
	Ma, Fanglan 1,2; Zhu, Changsheng1 ; Liu, Dukui 1
	2024
发表期刊	JOURNAL OF INTELLIGENT & FUZZY SYSTEMS
ISSN	1064-1246
卷号	46 期号:3 页码:6607-6617
摘要	Knowledge tracing (KT), which aims to trace human knowledge learning process by using machines, has widely applied in online learning systems. It dynamically models student's knowledge states in relation to different learning factors through their learning interactions. Recently, KT has attracted many researches attention due to its good performance to using deep learning. Although most of KT models have shown outstanding results, they have limitations: either ignore the human cognitive law and learning behavior, or lack the ability to go deeper modeling to trace knowledge state. In this paper, we propose a deeper knowledge tracking model integrating cognitive theory and learning behavior (CLDKT). It united the advantages of memory network and recurrent neural network of the existing deep learning KT models for modeling student learning. To better implement CLDKT, we add the residual network (ResNet) to realize the deep modeling of learning behaviors. Extensive experiments on three open benchmark datasets to evaluate our model. Experimental results demonstrate that (I) CLDKT outperforms the state-of-the-art KT models on students' performance prediction. (II) CLDKT can deeper modeling to trace knowledge state owing to the ResNet import. (III) CLDKT has better interpretability and predictability, which proves the effectiveness of the knowledge tracing model integrating cognitive law and learning behavior.
关键词	Knowledge tracing cognitive law learning behavior ResNet deep learning
DOI	10.3233/JIFS-235723
收录类别	SCIE ; EI
语种	英语
资助项目	Gansu Youth Science and Technology Fund Program [21JR11RA217, 22JR11RA208]; Outstanding Youth Fund Project of Gansu Academy of Sciences [2023 YQ-03]; Innovation Group Project of basic research in Gansu [23JRRA1348]
WOS研究方向	Computer Science
WOS类目	Computer Science, Artificial Intelligence
WOS记录号	WOS:001194580300070
出版者	IOS PRESS
EI入藏号	20241115753738
EI主题词	Recurrent neural networks
EI分类号	722.4 Digital Computers and Systems
原始文献类型	Article
EISSN	1875-8967
引用统计	无
文献类型	期刊论文
条目标识符	https://ir.lut.edu.cn/handle/2XXMBERH/170150
专题	计算机与通信学院
通讯作者	Zhu, Changsheng
作者单位	1.Lanzhou Univ Technol, Sch Comp & Commun, Lanzhou 730050, Peoples R China; 2.Gansu Acad Sci, Inst Sensing Technol, Lanzhou, Peoples R China
第一作者单位	兰州理工大学
通讯作者单位	兰州理工大学
第一作者的第一单位	兰州理工大学
推荐引用方式 GB/T 7714	Ma, Fanglan,Zhu, Changsheng,Liu, Dukui. A deeper knowledge tracking model integrating cognitive theory and learning behavior[J]. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS,2024,46(3):6607-6617.
APA	Ma, Fanglan,Zhu, Changsheng,&Liu, Dukui.(2024).A deeper knowledge tracking model integrating cognitive theory and learning behavior.JOURNAL OF INTELLIGENT & FUZZY SYSTEMS,46(3),6607-6617.
MLA	Ma, Fanglan,et al."A deeper knowledge tracking model integrating cognitive theory and learning behavior".JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 46.3(2024):6607-6617.