New Research Article (Preprint): “Metadata Analysis of Open Educational Resources”

The following research paper (preprint) was recently shared on arXiv.

Title

Metadata Analysis of Open Educational Resources

Authors

Mohammadreza Tavakoli
German National Library of Science and Technology (TIB)

Mirette Elias
Fraunhofer IAIS and University of Bonn

Gábor Kismihok
German National Library of Science and Technology (TIB)

Sören Auer
German National Library of Science and Technology (TIB)

Source

via arXiv

Note: “This paper has been accepted to be published in the 11th International Learning Analytics and Knowledge (LAK’2021), April 12–16, 2021. ACM. arXiv admin note: text overlap with arXiv:2005.10542″

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.

Direct to Full Text
9 pages; PDF.

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