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SCI."],"abstract":"<jats:title>Abstract<\/jats:title><jats:p>The Universal Declaration of Human Rights states that \u201ceveryone has the right to education\u201d and it must be \u201cequally accessible to all\u201d. Aligned with these principles are online learning platforms that offer free online courses that require no or minimal information from their students to democratize learning. Despite the benefits, the lack of user information challenges traditional recommendation systems and, when applied to education, impacts student experiences. This paper addresses the problem of limited student information in course recommendation systems. We tackle the problem by generating a semantically enriched vector-based representation of course content using an Open Knowledge Graph (DBpedia) and Topic Modeling methods, such as LDA and LSA and by collecting tracking events from log systems (Google Analytics) to create a hybrid recommendation system using content-based and collaborative filtering strategies for low-information scenarios. Our experiments use GCFGlobal.org, an online learning platform offering free self-paced online courses to 100 million people, to validate our approach. Results indicate that the proposed approach outperforms the previous works in the field contributing to the creation of fairer recommendation systems.<\/jats:p>","DOI":"10.1007\/s42979-023-02399-4","type":"journal-article","created":{"date-parts":[[2023,12,19]],"date-time":"2023-12-19T15:03:56Z","timestamp":1702998236000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["A Semantic Enhanced Course Recommender System via Knowledge Graphs for Limited User Information Scenarios"],"prefix":"10.1007","volume":"5","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0458-8142","authenticated-orcid":false,"given":"Juan","family":"Sanguino","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8742-2094","authenticated-orcid":false,"given":"Ruben","family":"Manrique","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4449-1705","authenticated-orcid":false,"given":"Olga","family":"Mari\u00f1o","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,12,19]]},"reference":[{"key":"2399_CR1","doi-asserted-by":"publisher","first-page":"129","DOI":"10.1007\/978-3-319-12024-9_17","volume-title":"Semantic web evaluation challenge","author":"T Di Noia","year":"2014","unstructured":"Di Noia T, Cantador I, Ostuni VC. Linked open data-enabled recommender systems: Eswc 2014 challenge on book recommendation. In: Presutti V, Stankovic M, Cambria E, Cantador I, Di Iorio A, Di Noia T, Lange C, Reforgiato Recupero D, Tordai A, editors. Semantic web evaluation challenge. Cham: Springer; 2014. p. 129\u201343."},{"key":"2399_CR2","doi-asserted-by":"publisher","unstructured":"Hassan J, Leong J, Schneider B. Multimodal data collection made easy: the ez-mmla toolkit: A data collection website that provides educators and researchers with easy access to multimodal data streams. In: 11th International learning analytics and knowledge conference. LAK21, ACM, New York; 2021. p. 579\u2013585. https:\/\/doi.org\/10.1145\/3448139.3448201","DOI":"10.1145\/3448139.3448201"},{"issue":"2","key":"2399_CR3","doi-asserted-by":"publisher","first-page":"405","DOI":"10.1007\/s10758-021-09541-2","volume":"27","author":"SN Kew","year":"2022","unstructured":"Kew SN, Tasir Z. Learning analytics in online learning environment: a systematic review on the focuses and the types of student-related analytics data. Technol Knowl Learn. 2022;27(2):405\u201327. https:\/\/doi.org\/10.1007\/s10758-021-09541-2.","journal-title":"Technol Knowl Learn"},{"key":"2399_CR4","doi-asserted-by":"publisher","unstructured":"Liao T, Feng X, Sun Y, Wang H, Liao C, Li Y. Online teaching platform based on big data recommendation system. In: Proceedings of the 5th international conference on information and education innovations. ICIEI \u201920, ACM, New York; 2020. p. 35\u201339. https:\/\/doi.org\/10.1145\/3411681.3412951.","DOI":"10.1145\/3411681.3412951"},{"issue":"1","key":"2399_CR5","doi-asserted-by":"publisher","first-page":"18","DOI":"10.1186\/s41039-021-00167-7","volume":"16","author":"B Ma","year":"2021","unstructured":"Ma B, Lu M, Taniguchi Y, Konomi S. Courseq: the impact of visual and interactive course recommendation in university environments. 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