兰州大学机构库
NTM-Based Skill-Aware Knowledge Tracing for Conjunctive Skills
Huang, Qiang1; Su, Wei1; Sun, Yuantao2; Huang, Tianyuan1; Shi, Juntai1
2022
Source PublicationComputational Intelligence and Neuroscience   Impact Factor & Quartile Of Published Year  The Latest Impact Factor & Quartile
ISSN1687-5265
EISSN1687-5273
Volume2022
page numbers16
AbstractKnowledge tracing (KT) is the task of modelling students' knowledge state based on their historical interactions on intelligent tutoring systems. Existing KT models ignore the relevance among the multiple knowledge concepts of a question and characteristics of online tutoring systems. This paper proposes a neural Turing machine-based skill-aware knowledge tracing (NSKT) for conjunctive skills, which can capture the relevance among the knowledge concepts of a question to model students' knowledge state more accurately and to discover more latent relevance among knowledge concepts effectively. We analyze the characteristics of the three real-world KT datasets in depth. Experiments on real-world datasets show that NSKT outperforms the state-of-the-art deep KT models on the AUC of prediction. This paper explores details of the prediction process of NSKT in modelling students' knowledge state, as well as the relevance of knowledge concepts and conditional influences between exercises. © 2022 Qiang Huang et al.
KeywordComputer aided instruction Online systems Students Intelligent tutoring Knowledge state Knowledge tracings Online tutoring Real-world State based Student knowledge Tracing model Tutoring system World knowledge
PublisherHindawi Limited
DOI10.1155/2022/9153697
Indexed ByEI ; SCIE
Language英语
WOS Research AreaMathematical & Computational Biology ; Neurosciences & Neurology
WOS SubjectMathematical & Computational Biology ; Neurosciences
WOS IDWOS:000855556500001
EI Accession Number20223312578196
EI KeywordsTuring machines
EI Classification Number721.1 Computer Theory, Includes Formal Logic, Automata Theory, Switching Theory, Programming Theory ; 722.4 Digital Computers and Systems ; 723.5 Computer Applications ; 901.2 Education
Original Document TypeJournal article (JA)
PMID 3593698
Citation statistics
Document Type期刊论文
Identifierhttps://ir.lzu.edu.cn/handle/262010/491739
Collection兰州大学
Corresponding AuthorSu, Wei
Affiliation
1.Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou, China;
2.College of Earth and Environmental Sciences, Lanzhou University, Lanzhou; 730000, China
Recommended Citation
GB/T 7714
Huang, Qiang,Su, Wei,Sun, Yuantao,et al. NTM-Based Skill-Aware Knowledge Tracing for Conjunctive Skills[J]. Computational Intelligence and Neuroscience,2022,2022.
APA Huang, Qiang,Su, Wei,Sun, Yuantao,Huang, Tianyuan,&Shi, Juntai.(2022).NTM-Based Skill-Aware Knowledge Tracing for Conjunctive Skills.Computational Intelligence and Neuroscience,2022.
MLA Huang, Qiang,et al."NTM-Based Skill-Aware Knowledge Tracing for Conjunctive Skills".Computational Intelligence and Neuroscience 2022(2022).
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