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Research on Deep Knowledge Tracing Model Integrating Graph Attention Network

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成果类型:
会议论文
作者:
Zhao, Zhongyuan;Liu, Zhaohui;Wang, Bei;Ouyang, Lijun;Wang, Can;...
作者机构:
[Ouyang, Lijun; Ouyang, Yan; Liu, Zhaohui; Wang, Can; Zhao, Zhongyuan] Univ South China, Sch Comp Sci, Hengyang, Peoples R China.
[Wang, Bei] Univ South China, Sch Language & Literature, Hengyang, Peoples R China.
语种:
英文
关键词:
knowledge tracing;graph attention network;interpretability
期刊:
2022 PROGNOSTICS AND HEALTH MANAGEMENT CONFERENCE, PHM-LONDON 2022
年:
2022
页码:
389-394
会议名称:
Prognostics and Health Management Conference (PHM-2022 London)
会议时间:
MAY 27-29, 2022
会议地点:
London, ENGLAND
会议主办单位:
[Zhao, Zhongyuan;Liu, Zhaohui;Ouyang, Lijun;Wang, Can;Ouyang, Yan] Univ South China, Sch Comp Sci, Hengyang, Peoples R China.^[Wang, Bei] Univ South China, Sch Language & Literature, Hengyang, Peoples R China.
主编:
Li, C Valentino, G Kang, L Cabrera, D Gjorgjevikj, D
出版地:
345 E 47TH ST, NEW YORK, NY 10017 USA
出版者:
IEEE
ISBN:
978-1-6654-7954-7
基金类别:
Funded Project of Hunan Provincial Department of Education [18C0413]; 2020 Hunan Provincial College Teaching Reform Research Project [HNJG2020-0477]; Scientific Research Project of Hunan Provincial Department of Education [20C1632]
机构署名:
本校为第一机构
摘要:
The current mainstream knowledge tracking model is based on the neural network of deep learning, which has a certain improvement in performance. However, due to the difficulty of interpretability of the deep learning methods, and the previous literature did not involve the high-dimensional information between problems and knowledge points when their model used the answer record, there is a situation that the relevant information is not sufficiently extracted. In order to solve the above problems, a knowledge tracing model based on the graph attention network mechanism is proposed, which uses t...

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