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Title: Quality Prediction of Open Educational Resources A Metadata-based Approach
Authors: Tavakoli, MohammadrezaElias, MiretteKismihók, GáborAuer, Sören
Editors: Chang, MaigaSampson, Demetrios G.Huang, RonghuaiHooshyar, DanialChen, Nian-ShingKinshukPedaste, Margus
Publishers Version: https://doi.org/10.1109/ICALT49669.2020.00007
Issue Date: 2020
Publisher: Piscataway, NJ : IEEE
Abstract: In the recent decade, online learning environments have accumulated millions of Open Educational Resources (OERs). However, for learners, finding relevant and high quality OERs is a complicated and time-consuming activity. Furthermore, metadata play a key role in offering high quality services such as recommendation and search. Metadata can also be used for automatic OER quality control as, in the light of the continuously increasing number of OERs, manual quality control is getting more and more difficult. In this work, we collected the metadata of 8,887 OERs to perform an exploratory data analysis to observe the effect of quality control on metadata quality. Subsequently, we propose an OER metadata scoring model, and build a metadata-based prediction model to anticipate the quality of OERs. Based on our data and model, we were able to detect high-quality OERs with the F1 score of 94.6%. © 20xx IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Keywords: OER; Open Educational Resources; metadata quality; OER quality; Big data; data analysis; quality prediction
DDC: 020
License: Es gilt deutsches Urheberrecht. Das Dokument darf zum eigenen Gebrauch kostenfrei genutzt, aber nicht im Internet bereitgestellt oder an Außenstehende weitergegeben werden.
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