Computer Science

Computer Science ›› 2023, Vol. 50 ›› Issue (3): 173-180.doi: 10.11896/jsjkx.211200134

• Database & Big Data & Data Science • Previous Articles     Next Articles

Graph Attention Deep Knowledge Tracing Model Integrated with IRT

DONG Yongfeng1,2,3, HUANG Gang1,2,3, XUE Wanruo1, LI Linhao1,2,3   

  1. 1 School of Artifcial Intelligence,Hebei University of Technology,Tianjin 300401,China
    2 Hebei Key Laboratory of Big Data Computing,Tianjin 300401,China
    3 Hebei Engineering Research Center of Data-Driven Industrial Intelligent,Tianjin 300401,China
  • Received:2021-12-13 Revised:2022-06-01 Online:2023-03-15 Published:2023-03-15
  • About author:DONG Yongfeng,born in 1977,Ph.D,professor,is a member of China Computer Federation.His main research interets include intelligent information processing,big data technology,robo-tics and intelligent control,and software engineering.
    LI Linhao,born in 1989,Ph.D,lecturer,is a member of China Computer Federation.His main research interests include quantization and hash learning,sparse signal recovery,background modeling and foreground detection.
  • Supported by:
    National Natural Science Foundation of China(61902106,61806072),Hebei Province Higher Education Teaching Reform Research and Practice Project(2020GJJG027) andNatural Science Foundation of Hebei Province(F2020202028).

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Abstract: Knowledge tracing aims to trace students’ knowledge state(the degree of knowledge) based on their historical answer performance in real time and predict their future answer performance.The current research only explores the direct influence of the question or concept itself on the performance of students’ answering questions,while often ignores the indirect influence of the deep-level information in the questions and the concepts contained on the performance of students’ answering questions.In order to make better use of these deep-level information,a graph attention deep knowledge tracing model integrated with IRT(GAKT-IRT) is proposed,which integrates item response theory(IRT).The graph attention network is applied to the field of knowledge tracing and uses IRT to increase the interpretability of the model.First,obtain the deep-level feature representation of the problem through the graph attention network layer.Next,model students’ knowledge state based on their historical answer sequence that combines the in-depth information.Then,use IRT to predict students’ future answer performance.Results of comparative experiments on 6 open real online education datasets prove that the GAKT-IRT model can better complete the knowledge tracing task and has obvious advantages in predicting the future performance of students in answering questions.

Key words: Knowledge tracing, Graph attention network, Item response theory, Deep learning, Interpretability

CLC Number: 

  • TP391.6
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