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Autor/inn/enXu, Wei; Zhou, Yuhan
TitelCourse Video Recommendation with Multimodal Information in Online Learning Platforms: A Deep Learning Framework
QuelleIn: British Journal of Educational Technology, 51 (2020) 5, S.1734-1747 (14 Seiten)Infoseite zur Zeitschrift
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Spracheenglisch
Dokumenttypgedruckt; online; Zeitschriftenaufsatz
ISSN0007-1013
DOI10.1111/bjet.12951
SchlagwörterElectronic Learning; Online Courses; Course Selection (Students); Preferences; Course Content; Feedback (Response); Video Technology
AbstractWith the rapid development of online learning platforms, learners have more access to various kinds of courses. However, they may find it difficult to make choices due to the massive number of courses. The main contribution of our research is the design of a course recommendation framework which extracts multimodal course features based on deep learning models. In this framework, different kinds of information of course, such as course title, and course audio and course comments, are used to make proper recommendation in online learning platforms. Moreover, we utilize both explicit and implicit feedback to infer learner's preference. Based on real-world datasets, our empirical results show that the proposed framework performs well in course recommendation, achieving an AUC score of 79.03%. This framework can provide technical support for course video recommendation, thus helping online learning platforms to manage course resources and optimize user learning experience. (As Provided).
AnmerkungenWiley. Available from: John Wiley & Sons, Inc. 111 River Street, Hoboken, NJ 07030. Tel: 800-835-6770; e-mail: cs-journals@wiley.com; Web site: https://www.wiley.com/en-us
Erfasst vonERIC (Education Resources Information Center), Washington, DC
Update2024/1/01
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