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Inf. Syst."],"published-print":{"date-parts":[[2026,2,28]]},"abstract":"<jats:p>\n                    The proliferation of Massive Open Online Courses (MOOCs) has created an urgent need for advanced course recommendation systems (RS). Course recommendations in MOOCs require transparent motivations to justify course selection, as there are often many courses with the same title, but which vary widely in content, duration, learning resources provided, and the academic authority of the instructor. Explainable recommendations are crucial to ensure that recommended courses fit well with learners\u2019 needs and increase the chance of successful course completion, but unfortunately existing RS for MOOCs struggle to provide explainable recommendations. In this article, we present\n                    <jats:italic toggle=\"yes\">KnowPath<\/jats:italic>\n                    , a novel RS for MOOCs, which generates effective and explainable recommendations. KnowPath uses open source Large Language Models (LLMs) to construct knowledge graphs (KGs) capable of accurately capturing complex relationships between MOOC entities (e.g., learners, instructors, educational resources) and employs Reinforcement Learning to align the output of an LLM with learner preferences. Extensive experiments on two public datasets (XueTang and COCO) demonstrate the superior performance and generalizability of\n                    <jats:italic toggle=\"yes\">KnowPath<\/jats:italic>\n                    , underlining its potential to revolutionize the field of personalized online education.\n                  <\/jats:p>","DOI":"10.1145\/3779436","type":"journal-article","created":{"date-parts":[[2025,12,12]],"date-time":"2025-12-12T03:00:37Z","timestamp":1765508437000},"page":"1-28","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["KnowPath: An LLM-Supported Knowledge Graph Construction and Path Finding Framework to Explainable MOOC Recommendations"],"prefix":"10.1145","volume":"44","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5959-390X","authenticated-orcid":false,"given":"Jia","family":"Zhu","sequence":"first","affiliation":[{"name":"Zhejiang Key Laboratory of Intelligent Education Technology and Application, Zhejiang Normal University, Jinhua, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-5061-6472","authenticated-orcid":false,"given":"Zhangze","family":"Chen","sequence":"additional","affiliation":[{"name":"Zhejiang Normal University, Jinhua, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7421-216X","authenticated-orcid":false,"given":"Pasquale De","family":"Meo","sequence":"additional","affiliation":[{"name":"University of Messina, Messina, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7989-8770","authenticated-orcid":false,"given":"Jueqi","family":"Guan","sequence":"additional","affiliation":[{"name":"Zhejiang Normal University, Jinhua, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7022-0020","authenticated-orcid":false,"given":"Zhongmei","family":"Han","sequence":"additional","affiliation":[{"name":"Zhejiang Normal University, Jinhua, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-8405-2910","authenticated-orcid":false,"given":"Weijie","family":"Shi","sequence":"additional","affiliation":[{"name":"Hong Kong University of Science and Technology, Hong Kong, China"}]}],"member":"320","published-online":{"date-parts":[[2026,1,9]]},"reference":[{"issue":"7","key":"e_1_3_2_2_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3543846","article-title":"Reinforcement learning based recommender systems: A survey","volume":"55","author":"Afsar Mohammad Mehdi","year":"2023","unstructured":"Mohammad Mehdi Afsar, Trafford Crump, and Behrouz H. 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A survey on reinforcement learning for recommender systems. IEEE Transactions on Neural Networks and Learning Systems 35, 10 (2024), 13164\u201313184.","journal-title":"IEEE Transactions on Neural Networks and Learning Systems"},{"key":"e_1_3_2_35_2","unstructured":"Nelson F. Liu Kevin Lin John Hewitt Ashwin Paranjape Mario Bevilacqua Fabio Petroni and Percy Liang. 2023. Lost in the middle: How language models use long contexts. arXiv:2307.03172. Retrieved from https:\/\/arxiv.org\/abs\/2307.03172"},{"key":"e_1_3_2_36_2","first-page":"13044","volume-title":"Proceedings of the Annual Meeting of the Association for Computational Linguistics (ACL \u201923)","author":"Lu Mengying","year":"2023","unstructured":"Mengying Lu, Yuquan Wang, Jifan Yu, Yexing Du, Lei Hou, and Juanzi Li. 2023. Distantly supervised course concept extraction in MOOCs with academic discipline. In Proceedings of the Annual Meeting of the Association for Computational Linguistics (ACL \u201923). Association for Computational Linguistics, 13044\u201313059."},{"key":"e_1_3_2_37_2","first-page":"583","volume-title":"Proceedings of the International Conference of Findings of the Association for Computational Linguistics: NAACL","author":"Lyu Hanjia","year":"2024","unstructured":"Hanjia Lyu, Song Jiang, Hanqing Zeng, Yinglong Xia, Qifan Wang, Si Zhang, Ren Chen, Christopher Leung, Jiajie Tang, and Jiebo Luo. 2024. LLM-Rec: Personalized recommendation via prompting large language models. In Proceedings of the International Conference of Findings of the Association for Computational Linguistics: NAACL. Association for Computational Linguistics, 583\u2013612."},{"key":"e_1_3_2_38_2","doi-asserted-by":"crossref","first-page":"350","DOI":"10.1145\/3375462.3375524","volume-title":"Proceedings of 10th International Conference on Learning Analytics and Knowledge (LAK \u201920)and","author":"Pardos Zachary A.","year":"2020","unstructured":"Zachary A. Pardos and Weijie Jiang. 2020. Designing for serendipity in a university course recommendation system. In Proceedings of 10th International Conference on Learning Analytics and Knowledge (LAK \u201920). Christoph Rensing and Hendrik Drachsler (Eds.), ACM, Frankfurt, Germany, 350\u2013359."},{"key":"e_1_3_2_39_2","first-page":"1817","volume-title":"Proceedings of the VLDB Endowment","volume":"16","author":"Piao Chengzhi","year":"2023","unstructured":"Chengzhi Piao, Tingyang Xu, Xiangguo Sun, Yu Rong, Kangfei Zhao, and Hong Cheng. 2023. Computing graph edit distance via neural graph matching. Proceedings of the VLDB Endowment 16, 8 (2023), 1817\u20131829."},{"key":"e_1_3_2_40_2","first-page":"10772","volume-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","author":"Pope Phillip E.","year":"2019","unstructured":"Phillip E. Pope, Soheil Kolouri, Mohammad Rostami, Charles E. Martin, and Heiko Hoffmann. 2019. Explainability methods for graph convolutional neural networks. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 10772\u201310781."},{"key":"e_1_3_2_41_2","doi-asserted-by":"crossref","first-page":"226","DOI":"10.1145\/3170358.3170400","volume-title":"Proceedings of 8th International Conference on Learning Analytics and Knowledge (LAK \u201918)","author":"Potts Boyd A.","year":"2018","unstructured":"Boyd A. Potts, Hassan Khosravi, Carl Reidsema, Aneesha Bakharia, Mark Belonogoff, and Melanie Fleming. 2018. Reciprocal peer recommendation for learning purposes. In Proceedings of 8th International Conference on Learning Analytics and Knowledge (LAK \u201918). ACM, 226\u2013235."},{"key":"e_1_3_2_42_2","unstructured":"John Schulman Filip Wolski Prafulla Dhariwal Alec Radford and Oleg Klimov. 2017. Proximal policy optimization algorithms. arXiv:1707.06347. Retrieved from https:\/\/arxiv.org\/abs\/1707.06347"},{"key":"e_1_3_2_43_2","doi-asserted-by":"crossref","first-page":"105618","DOI":"10.1016\/j.knosys.2020.105618","article-title":"A learning path recommendation model based on a multidimensional knowledge graph framework for e-learning","volume":"195","author":"Shi Daqian","year":"2020","unstructured":"Daqian Shi, Ting Wang, Hao Xing, and Hao Xu. 2020. A learning path recommendation model based on a multidimensional knowledge graph framework for e-learning. Knowledge Based Systems 195 (2020), 105618.","journal-title":"Knowledge Based Systems"},{"issue":"5","key":"e_1_3_2_44_2","doi-asserted-by":"crossref","first-page":"e70034","DOI":"10.1111\/exsy.70034","article-title":"Recommendation of learning resources for MOOCs based on historical sequential behaviours","volume":"42","author":"Song Wei","year":"2025","unstructured":"Wei Song, Qihao Zhang, Simon Fong, and Tengyue Li. 2025. Recommendation of learning resources for MOOCs based on historical sequential behaviours. Expert Systems 42, 5 (2025), e70034.","journal-title":"Expert Systems"},{"key":"e_1_3_2_45_2","doi-asserted-by":"crossref","first-page":"118","DOI":"10.1137\/1.9781611977172.14","volume-title":"Proceedings of the 2022 SIAM International Conference on Data Mining (SDM \u201922)","author":"Sun Hao","year":"2022","unstructured":"Hao Sun, Yuntao Li, and Yan Zhang. 2022. ConLearn: Contextual-knowledge-aware concept prerequisite relation learning with graph neural network. In Proceedings of the 2022 SIAM International Conference on Data Mining (SDM \u201922). SIAM, 118\u2013126."},{"issue":"3","key":"e_1_3_2_46_2","doi-asserted-by":"crossref","first-page":"313","DOI":"10.1109\/TLT.2021.3083180","article-title":"Capacity tracing-enhanced course recommendation in MOOCs","volume":"14","author":"Tian Xuetao","year":"2021","unstructured":"Xuetao Tian and Feng Liu. 2021. Capacity tracing-enhanced course recommendation in MOOCs. IEEE Transactions on Learning Technologies 14, 3 (2021), 313\u2013321.","journal-title":"IEEE Transactions on Learning Technologies"},{"key":"e_1_3_2_47_2","first-page":"4843","volume-title":"Proceedings of the ACM International Conference on Information and Knowledge Management (CIKM \u201923)","author":"Tu Shangqing","year":"2023","unstructured":"Shangqing Tu, Zheyuan Zhang, Jifan Yu, Chunyang Li, Siyu Zhang, Zijun Yao, Lei Hou, and Juanzi Li. 2023. LittleMu: Deploying an online virtual teaching assistant via heterogeneous sources integration and chain of teach prompts. In Proceedings of the ACM International Conference on Information and Knowledge Management (CIKM \u201923), 4843\u20134849."},{"key":"e_1_3_2_48_2","first-page":"1","volume-title":"Proceedings of the IEEE International Conference on Signal Processing, Informatics, Communication and Energy Systems (SPICES \u201915)","author":"Venkataraman Ganesh","year":"2015","unstructured":"Ganesh Venkataraman, Chellam Srinivasan, Arunkumar Ravichandran, Susan Elias, and Lakshimi Ramesh. 2015. Learning object recommendation for an effective open e-learning environment. In Proceedings of the IEEE International Conference on Signal Processing, Informatics, Communication and Energy Systems (SPICES \u201915). IEEE, 1\u20135."},{"key":"e_1_3_2_49_2","unstructured":"Xiang Wei Xingyu Cui Ning Cheng Xiaobin Wang Xin Zhang Shen Huang Pengjun Xie Jinan Xu Yufeng Chen Meishan Zhang et al. 2023. Zero-shot information extraction via chatting with ChatGPT. arXiv:2302.10205. 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Ros\u00e9. 2014. Peer influence on attrition in massively open online courses. In Proceedings of 7th International Conference on Educational Data Mining (EDM \u201914), 405\u2013406."},{"key":"e_1_3_2_53_2","doi-asserted-by":"crossref","first-page":"137","DOI":"10.1145\/3706468.3706486","volume-title":"Proceedings of 15th International Learning Analytics and Knowledge Conference (LAK \u201925)","author":"Yang Tianyuan","year":"2025","unstructured":"Tianyuan Yang, Baofeng Ren, Chenghao Gu, Boxuan Ma, Tianjia He, and Shin\u2019ichi Konomi. 2025. Towards better course recommendations: Integrating multi-perspective meta-paths and knowledge graphs. In Proceedings of 15th International Learning Analytics and Knowledge Conference (LAK \u201925). ACM, 137\u2013147."},{"key":"e_1_3_2_54_2","doi-asserted-by":"crossref","first-page":"125889","DOI":"10.1016\/j.eswa.2024.125889","article-title":"An explainable graph-based course recommendation model based on multiple interest factors","volume":"264","author":"Yang Yajing","year":"2025","unstructured":"Yajing Yang, Xicheng Peng, Mao Chen, and Sannyuya Liu. 2025. An explainable graph-based course recommendation model based on multiple interest factors. Expert Systems with Applications 264 (2025), 125889.","journal-title":"Expert Systems with Applications"},{"key":"e_1_3_2_55_2","doi-asserted-by":"publisher","DOI":"10.1145\/3597022"},{"key":"e_1_3_2_56_2","unstructured":"Hao Yuan Haiyang Yu Shurui Gui and Shuiwang Ji. 2020. Explainability in graph neural networks: A taxonomic survey. arXiV:2012.15445. Retrieved from https:\/\/arxiv.org\/abs\/2012.15445"},{"key":"e_1_3_2_57_2","doi-asserted-by":"crossref","unstructured":"Bowen Zhang and Harold Soh. 2024. Extract define canonicalize: An LLM-based framework for knowledge graph construction. arXiv:2404.03868. Retrieved from https:\/\/arxiv.org\/abs\/2404.03868","DOI":"10.18653\/v1\/2024.emnlp-main.548"},{"key":"e_1_3_2_58_2","doi-asserted-by":"publisher","unstructured":"Haonan Zhang Dongxia Wang Zhu Sun Yanhui Li Youcheng Sun Huizhi Liang and Wenhai Wang. 2025. KG4RecEval: Does knowledge graph really matter for recommender systems? ACM Transactions on Information Systems 43 3 Article 64 (Feb. 2025) 36. DOI: 10.1145\/3713071","DOI":"10.1145\/3713071"},{"key":"e_1_3_2_59_2","first-page":"435","volume-title":"Proceedings of 33rd AAAI Conference on Artificial Intelligence (AAAI \u201919), the 31st Innovative Applications of Artificial Intelligence Conference (IAAI \u201919), the 9th AAAI Symposium on Educational Advances in Artificial Intelligence (EAAI \u201919)","author":"Zhang Jing","year":"2019","unstructured":"Jing Zhang, Bowen Hao, Bo Chen, Cuiping Li, Hong Chen, and Jimeng Sun. 2019. 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