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Learning path generation algorithm based on knowledge graph attracted increasing attention of researchers. However, the current path recommendation algorithm based on knowledge graph only used a single relation to connect learning objects which could not generate different learning paths to meet the needs of different learners. To solve the problem, an adaptive learning path generation method combining knowledge graph and deep reinforcement learning was proposed. Firstly, a knowledge graph model combining fine-grained knowledge elements with students′ cognitive level was proposed, and the relationships among knowledge elements were predefined. Then, a path generation method based on dynamic reinforcement learning was proposed. Deep Q network(DQN) and double deep Q network(DDQN) were integrated into the framework of deep reinforcement learning through different weights. Experimental results showed that the proposed model could generate personalized learning paths that meet the requirements.
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Basic Information:
DOI：10.13705/j.issn.1671-6841.2022112
China Classification Code:G434;G353.1
Citation Information:
[1]ZHANG Han,ZHAO Runzhe,LU Peilong ,et al.Research on Adaptive Learning Path Generation Method for Intelligent Education[J].Journal of Zhengzhou University(Natural Science Edition),2022,54(06):59-65.DOI:10.13705/j.issn.1671-6841.2022112.
Fund Information:
河南省重大科技专项(201300210500); 郑州市重大科技创新专项(2020CXZX0053)
2022-04-19
2022
2023-03-17
2023
2022-07-08
1
2022-07-01
2022-07-01
2022-07-01