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Deep Knowledge Tracing Based on Spatial and Temporal Representation Learning for Learning Performance Prediction

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成果类型:
期刊论文
作者:
Lyu, Liting;Wang, Zhifeng;Yun, Haihong;Yang, Zexue;Li, Ya
通讯作者:
Zhifeng Wang
作者机构:
[Yang, Zexue; Yun, Haihong; Li, Ya; Lyu, Liting] Heilongjiang Inst Technol, Sch Comp Sci & Technol, Harbin 150050, Peoples R China.
[Wang, Zhifeng] Cent China Normal Univ, Sch Educ Informat Technol, Wuhan 430079, Peoples R China.
通讯机构:
[Zhifeng Wang] S
School of Educational Information Technology, Central China Normal University, Wuhan 430079, China<&wdkj&>Author to whom correspondence should be addressed.
语种:
英文
关键词:
prediction;learning performance;e-learning;deep learning;knowledge tracing;knowledge representation;spatial feature;temporal feature;convolutional neural network;bidirectional long short-term memory
期刊:
Applied Sciences-Basel
ISSN:
2076-3417
年:
2022
卷:
12
期:
14
页码:
7188-
基金类别:
Methodology research, L.L. and Z.W.; model realization, L.L.; supervision Z.W.; writing and editing, L.L. and Z.W.; data collection, L.L., Z.W., H.Y., Z.Y. and Y.L.; model evaluation, L.L., Z.W., H.Y., Z.Y. and Y.L.; funding acquisition, L.L., Z.W., H.Y., Z.Y. and Y.L. All authors have read and agreed to the published version of the manuscript. This research was funded in part by the Youth Science Foundation of Heilongjiang Institute of Technology (2021QJ07), the National Natural Science Foundation of China (Nos. 62177022, 61901165, 61501199), the Collaborative Innovation Center for Informatization and Balanced Development of K-12 Education by MOE and Hubei Province (No. xtzd2021-005), and Self-determined Research Funds of CCNU from the Colleges’ Basic Research and Operation of MOE (No. CCNU20ZT010), the Natural Science Foundation of Heilongjiang Province (LH2020F047), the Innovation Team Project of Heilongjiang Institute of Technology (2020CX07), the University Nursing Program for Young Scholars with Creative Talents in Heilongjiang Province (UNPYSCT-2020052), and the Education and Teaching Reform Research Project of Heilongjiang Institute of Technology (JG202109).
机构署名:
本校为其他机构
院系归属:
教育信息技术学院
摘要:
Knowledge tracing (KT) serves as a primary part of intelligent education systems. Most current KTs either rely on expert judgments or only exploit a single network structure, which affects the full expression of learning features. To adequately mine features of students’ learning process, Deep Knowledge Tracing Based on Spatial and Temporal Deep Representation Learning for Learning Performance Prediction (DKT-STDRL) is proposed in this paper. DKT-STDRL extracts spatial features from students’ learning history sequence, and then further extrac...

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