{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T15:53:47Z","timestamp":1775058827067,"version":"3.50.1"},"reference-count":32,"publisher":"World Scientific Pub Co Pte Ltd","issue":"01","funder":[{"DOI":"10.13039\/501100003787","name":"Natural Science Foundation of Hebei Province","doi-asserted-by":"publisher","award":["F2018208116"],"award-info":[{"award-number":["F2018208116"]}],"id":[{"id":"10.13039\/501100003787","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Key Project of Hebei Education Department","award":["ZD2021048"],"award-info":[{"award-number":["ZD2021048"]}]},{"DOI":"10.13039\/501100003787","name":"Natural Science Foundation of Hebei Province","doi-asserted-by":"publisher","award":["F2022208002"],"award-info":[{"award-number":["F2022208002"]}],"id":[{"id":"10.13039\/501100003787","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Int. J. Soft. Eng. Knowl. Eng."],"published-print":{"date-parts":[[2023,1]]},"abstract":"<jats:p>In e-learning, the increasing number of learning resources makes it difficult for learners to find suitable learning resources. In addition, learners may have different preferences and cognitive abilities for learning resources, where differences in learners\u2019 cognitive abilities will lead to different importance of learning resources. Therefore, recommending personalized learning paths for learners has become a research hotspot. Considering learners\u2019 preferences and the importance of learning resources, this paper proposes a learning path recommendation algorithm based on knowledge graph. We construct a multi-dimensional courses knowledge graph in computer field (MCCKG), and then propose a method based on graph convolutional network for modeling high-order correlations on the knowledge graph to more accurately capture learners\u2019 preferences. Furthermore, the importance of learning resources is calculated by using the characteristics of learning resources in the MCCKG and learners\u2019 characteristics. Finally, by weighting the two factors of learners\u2019 preferences and the importance of learning resources, we recommend the optimal learning path for learners. Our method is evaluated from the aspects of learner\u2019s satisfaction, algorithm effectiveness, etc. The experimental results show that the method proposed in this paper can recommend a personalized learning path to satisfy the needs of learners, thus reducing the workload of manually planning learning paths.<\/jats:p>","DOI":"10.1142\/s0218194022500681","type":"journal-article","created":{"date-parts":[[2022,10,28]],"date-time":"2022-10-28T04:08:25Z","timestamp":1666930105000},"page":"109-131","source":"Crossref","is-referenced-by-count":47,"title":["Personalized Learning Path Recommendation for E-Learning Based on Knowledge Graph and Graph Convolutional Network"],"prefix":"10.1142","volume":"33","author":[{"given":"Xiaoming","family":"Zhang","sequence":"first","affiliation":[{"name":"School of Information Science and Engineering, Hebei University of Science and Technology, 26 Yuxiang Street, Shijiazhuang, Hebei Province 050018, P.\u00a0R.\u00a0China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6849-7509","authenticated-orcid":false,"given":"Shan","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Information Science and Engineering, Hebei University of Science and Technology, 26 Yuxiang Street, Shijiazhuang, Hebei Province 050018, P.\u00a0R.\u00a0China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3107-729X","authenticated-orcid":false,"given":"Huiyong","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Information Science and Engineering, Hebei University of Science and Technology, 26 Yuxiang Street, Shijiazhuang, Hebei Province 050018, P.\u00a0R.\u00a0China"}]}],"member":"219","published-online":{"date-parts":[[2022,12,22]]},"reference":[{"issue":"1","key":"S0218194022500681BIB001","first-page":"33","volume":"23","author":"Ali S.","year":"2022","journal-title":"Egypt. 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