{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,14]],"date-time":"2026-04-14T12:54:09Z","timestamp":1776171249875,"version":"3.50.1"},"reference-count":30,"publisher":"SAGE Publications","issue":"3","license":[{"start":{"date-parts":[[2025,1,15]],"date-time":"2025-01-15T00:00:00Z","timestamp":1736899200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Journal of Computational Methods in Sciences and Engineering"],"published-print":{"date-parts":[[2025,5]]},"abstract":"<jats:p>In e-learning, the rapid expansion of learning resources poses challenges for learners in finding suitable materials due to their diverse preferences and cognitive abilities. Consequently, personalized learning path recommendation has emerged as a pivotal research area, especially for advancing e-learning systems. This paper introduces an algorithmic framework that integrates deep reinforcement learning with a graph attention mechanism to tailor learning paths to individual learners. The online course dataset is selected and a series of controlled experiments are conducted on the common recommendation models proposed in the past, and the experimental results are analyzed using a combination of two evaluation indices, such as data evaluation results and model variance. The experimental results show that adding the attention mechanism can significantly improve the accuracy of the model recommendation, compared with the deep reinforcement learning model without adding the graph attention mechanism, the comprehensive scores of the students in the test set were improved by 5.8 and 12.8 points, respectively, and the accuracy was improved by 5.3% compared with the previous deep learning model; the deep reinforcement model used in this paper with the addition of the labeling feedback mechanism was improved by 5.3% compared with the deep learning with feedback mechanism. In the recommendation model, the final scores of the students were improved by 3.7 and 8.2 points, respectively. In addition, the Advanced test set in the recommendation model of the learning path recommended by the student score improvement is more than two times of the Middle test set\u2019s scores improvement, indicating that more learning recommended object knowledge points the richer, the model recommendation accuracy rate is higher. By merging graph attention mechanisms with deep reinforcement learning, our system provides precise recommendations, offering insights into the development of efficient personalized learning path systems and accelerating their educational applications.<\/jats:p>","DOI":"10.1177\/14727978241313260","type":"journal-article","created":{"date-parts":[[2025,1,15]],"date-time":"2025-01-15T22:36:46Z","timestamp":1736980606000},"page":"2411-2426","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":3,"title":["Personalized learning path based on graph attention mechanism deep reinforcement learning research on recommender systems"],"prefix":"10.1177","volume":"25","author":[{"given":"Runlong","family":"Gu","sequence":"first","affiliation":[{"name":"Lanzhou Resources & Environment Voc-Tech University"}]}],"member":"179","published-online":{"date-parts":[[2025,1,15]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.3390\/app13105946"},{"key":"e_1_3_2_3_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.chb.2015.02.014"},{"key":"e_1_3_2_4_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2020.113596"},{"key":"e_1_3_2_5_2","first-page":"12","article-title":"A step-by-step guide to personalize learning","volume":"40","author":"Bray B","year":"2013","unstructured":"Bray B, McClaskey K. A step-by-step guide to personalize learning. Learn Lead Technol 2013; 40: 12\u201319.","journal-title":"Learn Lead Technol"},{"key":"e_1_3_2_6_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-43247-7_17"},{"issue":"33","key":"e_1_3_2_7_2","first-page":"109","article-title":"Personalized learning path recommendation for E-learning based on knowledge graph and graph convolutional network","volume":"33","author":"Zhang X","year":"2022","unstructured":"Zhang X, Liu S, Wang H. Personalized learning path recommendation for E-learning based on knowledge graph and graph convolutional network. Int J Software Eng Knowl Eng 2022; 33(33): 109\u2013131.","journal-title":"Int J Software Eng Knowl Eng"},{"key":"e_1_3_2_8_2","doi-asserted-by":"crossref","unstructured":"Cai D Zhang Y Dai B. Learning path recommendation based on knowledge tracing model and reinforcement learning. In: 2019 IEEE 5th International Conference on Computer and Communications (ICCC) Chengdu China 06\u201309 December 2019 pp. 1881\u20131885.","DOI":"10.1109\/ICCC47050.2019.9064104"},{"key":"e_1_3_2_9_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2020.105910"},{"key":"e_1_3_2_10_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-981-19-4453-6_4"},{"key":"e_1_3_2_11_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2018.02.053"},{"key":"e_1_3_2_12_2","doi-asserted-by":"publisher","DOI":"10.1007\/s40692-022-00250-y"},{"key":"e_1_3_2_13_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-024-54729-y"},{"key":"e_1_3_2_14_2","doi-asserted-by":"publisher","DOI":"10.1080\/10494820.2021.1937659"},{"key":"e_1_3_2_15_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11063-020-10404-7"},{"key":"e_1_3_2_16_2","doi-asserted-by":"publisher","DOI":"10.32604\/cmc.2020.09913"},{"key":"e_1_3_2_17_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11063-023-11310-4"},{"key":"e_1_3_2_18_2","first-page":"053034","article-title":"LNGAT: local neighborhood graph attention network","volume":"31","author":"Sun Y","year":"2022","unstructured":"Sun Y, Ma H, Ko YB, et al. LNGAT: local neighborhood graph attention network. J Electron Imag 2022; 31: 053034.","journal-title":"J Electron Imag"},{"key":"e_1_3_2_19_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2023.110335"},{"key":"e_1_3_2_20_2","doi-asserted-by":"publisher","DOI":"10.1109\/MSP.2017.2743240"},{"key":"e_1_3_2_21_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.3044343"},{"key":"e_1_3_2_22_2","first-page":"3940","article-title":"Graph contextualized self-attention network for session-based recommendation","volume":"1","author":"Xu C","year":"2019","unstructured":"Xu C, Zhao P, Liu Y, et al. Graph contextualized self-attention network for session-based recommendation. Proceedings of the 28th International Joint Conference on Artificial Intelligence 2019; 1: 3940\u20133946.","journal-title":"Proceedings of the 28th International Joint Conference on Artificial Intelligence"},{"issue":"34","key":"e_1_3_2_23_2","first-page":"3946","article-title":"Personalized graph neural networks with attention mechanism for session-aware recommendation","volume":"34","author":"Zhang M","year":"2020","unstructured":"Zhang M, Wu S, Gao M, et al. Personalized graph neural networks with attention mechanism for session-aware recommendation. IEEE Trans Knowl Data Eng 2020; 34(34): 3946\u20133957.","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"e_1_3_2_24_2","doi-asserted-by":"publisher","DOI":"10.1145\/3158369"},{"key":"e_1_3_2_25_2","first-page":"17","article-title":"Deep learning methods on recommender system: A survey of state-of-the-art","volume":"162","author":"Basiliyos TB","year":"2017","unstructured":"Basiliyos TB, Charles AO, Bernabe B. Deep learning methods on recommender system: A survey of state-of-the-art. Int J Comput Appl 2017; 162: 17\u201322.","journal-title":"Int J Comput Appl"},{"key":"e_1_3_2_26_2","doi-asserted-by":"crossref","unstructured":"Yi B Zhang D Wang Y et al. Research on personalized learning model under informatization environment. In: 2017 international symposium on educational technology Hong Kong China 27\u201329 June 2017 pp. 48\u201352.","DOI":"10.1109\/ISET.2017.19"},{"key":"e_1_3_2_27_2","doi-asserted-by":"crossref","unstructured":"Yang Q Yuan D Zhang J. The research of personalized learning system based on learner interests and cognitive level. In: 2014 9th international conference on computer science & education Vancouver BC Canada 22\u201324 August 2014 pp. 522\u2013526.","DOI":"10.1109\/ICCSE.2014.6926516"},{"key":"e_1_3_2_28_2","doi-asserted-by":"crossref","unstructured":"Ghazali ASM Noor SFM Saad S. Review of personalized learning approaches and methods in e-learning environment. In: 2015International Conference on Electrical Engineering and Informatics Denpasar Indonesia 10\u201311 August 2015 pp. 624\u2013627.","DOI":"10.1109\/ICEEI.2015.7352574"},{"key":"e_1_3_2_29_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.is.2021.101978"},{"key":"e_1_3_2_30_2","first-page":"435","article-title":"Hierarchical reinforcement learning for course recommendation in MOOCs","volume":"54","author":"Zhang J","year":"2019","unstructured":"Zhang J, Hao B, Chen B, et al. Hierarchical reinforcement learning for course recommendation in MOOCs. Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence and Thirty-First Innovative Applications of Artificial Intelligence Conference and Ninth AAAI Symposium on Educational Advances in Artificial Intelligence 2019; 54: 435\u2013442.","journal-title":"Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence and Thirty-First Innovative Applications of Artificial Intelligence Conference and Ninth AAAI Symposium on Educational Advances in Artificial Intelligence"},{"key":"e_1_3_2_31_2","doi-asserted-by":"publisher","DOI":"10.1145\/3636555.3636898"}],"container-title":["Journal of Computational Methods in Sciences and Engineering"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.1177\/14727978241313260","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/full-xml\/10.1177\/14727978241313260","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.1177\/14727978241313260","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,13]],"date-time":"2026-02-13T16:31:01Z","timestamp":1771000261000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/10.1177\/14727978241313260"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,1,15]]},"references-count":30,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2025,5]]}},"alternative-id":["10.1177\/14727978241313260"],"URL":"https:\/\/doi.org\/10.1177\/14727978241313260","relation":{},"ISSN":["1472-7978","1875-8983"],"issn-type":[{"value":"1472-7978","type":"print"},{"value":"1875-8983","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,1,15]]}}}