Abstract
Constructing a personalized learning path is a critical way to solve the problem of cognitive difference and learning disorientation effectively. The construction process of the learning path is closely related to the internal relationship between knowledge and needs to meet different learning scenarios and learning needs. Because of the above requirements, a personalized learning path model based on a knowledge network is proposed in this paper. The algorithm begins by building a knowledge network with learning resource nodes and knowledge points. Following that, the order of the knowledge points was determined using their sequential link. A sequence of learning materials that adhere to user characteristics was eventually acquired by evaluating the learning time limit of various learning contexts. The proposed approach was tested on the data sets of open MOOPer and Python learning platforms. Compared with traditional learning path construction algorithms, the proposed algorithm improves the accuracy and relevance.
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Acknowledgements
The research is supported by the National Natural Science Foundation of China (No. 72174079, No. 1210050123, No. 72101045), Natural Science Foundation of the Jiangsu Higher Education Institutions of China (No. 19KJB520004, No. 21KJB520033), Jiangsu Province “333” project (BRA2020261), and Lianyungang “521 projects,” Science and Technology project of Lianyungang High-tech Zone (No. ZD201912).
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Li, H., Gong, R., Zhong, Z. et al. Research on personalized learning path planning model based on knowledge network. Neural Comput & Applic 35, 8809–8821 (2023). https://doi.org/10.1007/s00521-022-07658-8
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DOI: https://doi.org/10.1007/s00521-022-07658-8