short-paper

Open access

Metadata Analysis of Open Educational Resources

Authors:

Mohammadreza Tavakoli,

G�bor Kismih�k,

S�ren AuerAuthors Info & Claims

LAK21: LAK21: 11th International Learning Analytics and Knowledge Conference

Pages 626 - 631

https://doi.org/10.1145/3448139.3448208

Published: 12 April 2021 Publication History

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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.

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Cited By

Ovtšarenko O(2023)GENERATION OF A LEARNING PATH IN E-LEARNING ENVIRONMENTS: LITERATURE REVIEWNew Trends in Computer Sciences10.3846/ntcs.2023.182781:1(32-50)Online publication date: 11-Apr-2023
https://doi.org/10.3846/ntcs.2023.18278
Segarra-Faggioni VRomero-Pelaez A(2022)Automatic classification of OER for metadata quality assessment2022 International Conference on Advanced Learning Technologies (ICALT)10.1109/ICALT55010.2022.00011(16-18)Online publication date: Jul-2022
https://doi.org/10.1109/ICALT55010.2022.00011
Perez-Colado IPerez-Colado VMartinez-Ortiz IFreire MFernandez-Manjon B(2022)Using e-Learning Standards to Improve Serious Game Deployment and Evaluation2022 IEEE Global Engineering Education Conference (EDUCON)10.1109/EDUCON52537.2022.9766573(2077-2083)Online publication date: 28-Mar-2022
https://doi.org/10.1109/EDUCON52537.2022.9766573
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Published In

cover image ACM Other conferences

LAK21: LAK21: 11th International Learning Analytics and Knowledge Conference

April 2021

645 pages

ISBN:9781450389358

DOI:10.1145/3448139

Program Chairs:
Maren Scheffel
Ruhr University Bochum, Germany
,
Nia Dowell
University of California, Irvine, USA
,
Srecko Joksimovic
University of South Australia, Australia
,
George Siemens
University of Texas, Arlington, USA & University of South Australia, Australia

Copyright � 2021 Owner/Author.

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike International 4.0 License.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 12 April 2021

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LAK21

LAK21: 11th International Learning Analytics and Knowledge Conference

April 12 - 16, 2021

CA, Irvine, USA

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Overall Acceptance Rate 236 of 782 submissions, 30%

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Cited By

Ovtšarenko O(2023)GENERATION OF A LEARNING PATH IN E-LEARNING ENVIRONMENTS: LITERATURE REVIEWNew Trends in Computer Sciences10.3846/ntcs.2023.182781:1(32-50)Online publication date: 11-Apr-2023
https://doi.org/10.3846/ntcs.2023.18278
Segarra-Faggioni VRomero-Pelaez A(2022)Automatic classification of OER for metadata quality assessment2022 International Conference on Advanced Learning Technologies (ICALT)10.1109/ICALT55010.2022.00011(16-18)Online publication date: Jul-2022
https://doi.org/10.1109/ICALT55010.2022.00011
Perez-Colado IPerez-Colado VMartinez-Ortiz IFreire MFernandez-Manjon B(2022)Using e-Learning Standards to Improve Serious Game Deployment and Evaluation2022 IEEE Global Engineering Education Conference (EDUCON)10.1109/EDUCON52537.2022.9766573(2077-2083)Online publication date: 28-Mar-2022
https://doi.org/10.1109/EDUCON52537.2022.9766573
Tavakoli MFaraji AVrolijk JMolavi MMol SKismih�k G(2022)An AI-based open recommender system for personalized labor market driven educationAdvanced Engineering Informatics10.1016/j.aei.2021.10150852:COnline publication date: 1-Apr-2022
https://dl.acm.org/doi/10.1016/j.aei.2021.101508
Behr AMendes ATrigo PCascalho JGuerra HCosta AParente MBotelho AVicari R(2021)Recommending Metadata Contents for Learning Objects Through Linked DataHighlights in Practical Applications of Agents, Multi-Agent Systems, and Social Good. The PAAMS Collection10.1007/978-3-030-85710-3_10(115-126)Online publication date: 27-Sep-2021
https://doi.org/10.1007/978-3-030-85710-3_10

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