Quality Prediction of Open Educational Resources: A Metadata-based Approach

Research output: Chapter in book/report/conference proceedingConference contributionResearchpeer review

Authors

  • Mohammadreza Tavakoli
  • Mirette Elias
  • Gabor Kismihok
  • Soren Auer

External Research Organisations

  • German National Library of Science and Technology (TIB)
  • University of Bonn
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Details

Original languageEnglish
Title of host publicationProceedings - IEEE 20th International Conference on Advanced Learning Technologies, ICALT 2020
EditorsMaiga Chang, Demetrios G Sampson, Ronghuai Huang, Danial Hooshyar, Nian-Shing Chen, Kinshuk Kinshuk, Margus Pedaste
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages29-31
Number of pages3
ISBN (print)9781728160900
Publication statusPublished - 2020
Externally publishedYes
Event20th IEEE International Conference on Advanced Learning Technologies, ICALT 2020 - Virtual, Online, Estonia
Duration: 6 Jul 20209 Jul 2020

Publication series

NameProceedings - International Conference on Advanced Learning Technologies (ICALT)
ISSN (Print)2161-377X

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%.

Keywords

    Big data, Data analysis, Metadata quality, OER, OER quality, Open educational resources, Quality prediction

ASJC Scopus subject areas

Cite this

Quality Prediction of Open Educational Resources: A Metadata-based Approach. / Tavakoli, Mohammadreza; Elias, Mirette; Kismihok, Gabor et al.
Proceedings - IEEE 20th International Conference on Advanced Learning Technologies, ICALT 2020. ed. / Maiga Chang; Demetrios G Sampson; Ronghuai Huang; Danial Hooshyar; Nian-Shing Chen; Kinshuk Kinshuk; Margus Pedaste. Institute of Electrical and Electronics Engineers Inc., 2020. p. 29-31 9155928 (Proceedings - International Conference on Advanced Learning Technologies (ICALT)).

Research output: Chapter in book/report/conference proceedingConference contributionResearchpeer review

Tavakoli, M, Elias, M, Kismihok, G & Auer, S 2020, Quality Prediction of Open Educational Resources: A Metadata-based Approach. in M Chang, DG Sampson, R Huang, D Hooshyar, N-S Chen, K Kinshuk & M Pedaste (eds), Proceedings - IEEE 20th International Conference on Advanced Learning Technologies, ICALT 2020., 9155928, Proceedings - International Conference on Advanced Learning Technologies (ICALT), Institute of Electrical and Electronics Engineers Inc., pp. 29-31, 20th IEEE International Conference on Advanced Learning Technologies, ICALT 2020, Virtual, Online, Estonia, 6 Jul 2020. https://doi.org/10.1109/ICALT49669.2020.00007
Tavakoli, M., Elias, M., Kismihok, G., & Auer, S. (2020). Quality Prediction of Open Educational Resources: A Metadata-based Approach. In M. Chang, D. G. Sampson, R. Huang, D. Hooshyar, N.-S. Chen, K. Kinshuk, & M. Pedaste (Eds.), Proceedings - IEEE 20th International Conference on Advanced Learning Technologies, ICALT 2020 (pp. 29-31). Article 9155928 (Proceedings - International Conference on Advanced Learning Technologies (ICALT)). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICALT49669.2020.00007
Tavakoli M, Elias M, Kismihok G, Auer S. Quality Prediction of Open Educational Resources: A Metadata-based Approach. In Chang M, Sampson DG, Huang R, Hooshyar D, Chen NS, Kinshuk K, Pedaste M, editors, Proceedings - IEEE 20th International Conference on Advanced Learning Technologies, ICALT 2020. Institute of Electrical and Electronics Engineers Inc. 2020. p. 29-31. 9155928. (Proceedings - International Conference on Advanced Learning Technologies (ICALT)). doi: 10.1109/ICALT49669.2020.00007
Tavakoli, Mohammadreza ; Elias, Mirette ; Kismihok, Gabor et al. / Quality Prediction of Open Educational Resources : A Metadata-based Approach. Proceedings - IEEE 20th International Conference on Advanced Learning Technologies, ICALT 2020. editor / Maiga Chang ; Demetrios G Sampson ; Ronghuai Huang ; Danial Hooshyar ; Nian-Shing Chen ; Kinshuk Kinshuk ; Margus Pedaste. Institute of Electrical and Electronics Engineers Inc., 2020. pp. 29-31 (Proceedings - International Conference on Advanced Learning Technologies (ICALT)).
Download
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T2 - 20th IEEE International Conference on Advanced Learning Technologies, ICALT 2020

AU - Tavakoli, Mohammadreza

AU - Elias, Mirette

AU - Kismihok, Gabor

AU - Auer, Soren

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N2 - 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%.

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KW - Metadata quality

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BT - Proceedings - IEEE 20th International Conference on Advanced Learning Technologies, ICALT 2020

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