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Title: Metadata analysis of open educational resources
Authors: Tavakoli, MohammadrezaElias, MiretteKismihók, GáborAuer, Sören
Editors: Scheffel, Maren
Publishers version: https://doi.org/10.1145/3448139.3448208
URI: https://oa.tib.eu/renate/handle/123456789/10137
http://dx.doi.org/10.34657/9175
Issue Date: 2021
Published in: ACM Digital Library
Book: LAK21: 11th International Learning Analytics and Knowledge Conference
Journal: ACM Digital Library
Page Start: 626
Page End: 631
Publisher: New York,NY,United States : Association for Computing Machinery
Abstract: Open Educational Resources (OERs) are openly licensed educational materials that are widely used for learning. Nowadays, many online learning repositories provide millions of OERs. Therefore, it is exceedingly difficult for learners to find the most appropriate OER among these resources. Subsequently, the precise OER metadata is critical for providing high-quality services such as search and recommendation. Moreover, metadata facilitates the process of automatic OER quality control as the continuously increasing number of OERs makes manual quality control extremely difficult. This work uses the metadata of 8,887 OERs to perform an exploratory data analysis on OER metadata. Accordingly, this work proposes metadata-based scoring and prediction models to anticipate the quality of OERs. Based on the results, our analysis demonstrated that OER metadata and OER content qualities are closely related, as we could detect high-quality OERs with an accuracy of 94.6%. Our model was also evaluated on 884 educational videos from Youtube to show its applicability on other educational repositories.
Keywords: Exploratory analysis; Machine learning; Metadata analysis; OER; Open educational resources; Prediction models; Konferenzschrift
Type: bookPart; Text
Publishing status: publishedVersion
DDC: 004
License: CC BY-NC-SA 4.0 Unported
Link to license: https://creativecommons.org/licenses/by-nc-sa/4.0/
Appears in Collections:Informatik
Informationswissenschaften

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Tavakoli, Mohammadreza, Mirette Elias, Gábor Kismihók and Sören Auer, 2021. Metadata analysis of open educational resources. In: (Hrsg.)Maren Scheffel. New York,NY,United States : Association for Computing Machinery. ISBN 978-1-4503-8935-8
Tavakoli, M., Elias, M., Kismihók, G. and Auer, S. (2021) “Metadata analysis of open educational resources.” New York,NY,United States : Association for Computing Machinery. doi: https://doi.org/10.1145/3448139.3448208.
Tavakoli M, Elias M, Kismihók G, Auer S. Metadata analysis of open educational resources. In: , editorScheffel M. New York,NY,United States : Association for Computing Machinery; 2021.
Tavakoli, M., Elias, M., Kismihók, G., & Auer, S. (2021). Metadata analysis of open educational resources. New York,NY,United States : Association for Computing Machinery. https://doi.org/https://doi.org/10.1145/3448139.3448208
Tavakoli M, Elias M, Kismihók G, Auer S. Metadata analysis of open educational resources. In: , ed.Scheffel M New York,NY,United States : Association for Computing Machinery; 2021. doi:https://doi.org/10.1145/3448139.3448208


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