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Intell. Syst. Technol."],"published-print":{"date-parts":[[2023,6,30]]},"abstract":"<jats:p>In this article, we study knowledge concept recommendation in Massive Open Online Courses (MOOCs) in an explainable manner. Knowledge concepts, composing course units (e.g., videos) in MOOCs, refer to topics and skills that students are expected to master. Compared to traditional course recommendation in MOOCs, knowledge concepts recommendation has drawn more attention because students\u2019 interests over knowledge concepts can better revealstudents\u2019 real intention in a more refined granularity. However, there are three unique challenges in knowledge concept recommendation: (1) How to design an appropriate data structure to capture complex relationships between knowledge concepts, course units, and other participants (e.g., students, teachers)? (2) How to model interactions between students and knowledge concepts? (3) How to make explainable recommendation results to students? To tackle these challenges, we formulate the knowledge concept recommendation as a reinforcement learning task integrated with MOOC knowledge graph (KG). Specifically, we first construct MOOC KG as the environment to capture all the relationships and behavioral histories by considering all the entities (e.g., students, teachers, videos, courses, and knowledge concepts) on the MOOC provider. Then, to model the interactions between students and knowledge concepts, we train an agent to mimic students\u2019 learning behavioral patterns facing the complex environment. Moreover, to provide explainable recommendation results, we generate recommended knowledge concepts in the format of a path from MOOC KG to indicate semantic reasons. Finally, we conduct extensive experiments on a real-world MOOC dataset to demonstrate the effectiveness of our proposed method.<\/jats:p>","DOI":"10.1145\/3579991","type":"journal-article","created":{"date-parts":[[2023,2,1]],"date-time":"2023-02-01T12:10:55Z","timestamp":1675253455000},"page":"1-20","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":35,"title":["Reinforced Explainable Knowledge Concept Recommendation in MOOCs"],"prefix":"10.1145","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4529-8114","authenticated-orcid":false,"given":"Lu","family":"Jiang","sequence":"first","affiliation":[{"name":"Northeast Normal University, Changchun, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6053-5977","authenticated-orcid":false,"given":"Kunpeng","family":"Liu","sequence":"additional","affiliation":[{"name":"Portland State University, Portland, OR, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5890-9255","authenticated-orcid":false,"given":"Yibin","family":"Wang","sequence":"additional","affiliation":[{"name":"Northeast Normal University, Changchun, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3948-0059","authenticated-orcid":false,"given":"Dongjie","family":"Wang","sequence":"additional","affiliation":[{"name":"University of Central Florida, Orlando, FL, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3961-5523","authenticated-orcid":false,"given":"Pengyang","family":"Wang","sequence":"additional","affiliation":[{"name":"University of Macau, Macau S.A.R., China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1767-8024","authenticated-orcid":false,"given":"Yanjie","family":"Fu","sequence":"additional","affiliation":[{"name":"University of Central Florida, Orlando, FL, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6226-2394","authenticated-orcid":false,"given":"Minghao","family":"Yin","sequence":"additional","affiliation":[{"name":"Northeast Normal University, Changchun, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2023,4]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2005.99"},{"key":"e_1_3_2_3_2","doi-asserted-by":"publisher","DOI":"10.7763\/IJIET.2016.V6.745"},{"key":"e_1_3_2_4_2","first-page":"425","volume-title":"Portuguese Conference on Artificial Intelligence","author":"Byd\u017eovsk\u00e1 Hana","year":"2015","unstructured":"Hana Byd\u017eovsk\u00e1. 2015. 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