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Title: Extracting Topics from Open Educational Resources
Authors: Molavi, MohammadrezaTavakoli, MohammadrezaKismihók, Gábor
URI: https://oa.tib.eu/renate/handle/123456789/6170
https://doi.org/10.34657/5217
Issue Date: 2020
Published in: arXiv (2020)
Publisher: Ithaca, NY : Cornell University
Abstract: In recent years, Open Educational Resources (OERs) were earmarked as critical when mitigating the increasing need for education globally. Obviously, OERs have high-potential to satisfy learners in many different circumstances, as they are available in a wide range of contexts. However, the low-quality of OER metadata, in general, is one of the main reasons behind the lack of personalised services such as search and recommendation. As a result, the applicability of OERs remains limited. Nevertheless, OER metadata about covered topics (subjects) is essentially required by learners to build effective learning pathways towards their individual learning objectives. Therefore, in this paper, we report on a work in progress project proposing an OER topic extraction approach, applying text mining techniques, to generate high-quality OER metadata about topic distribution. This is done by: 1) collecting 123 lectures from Coursera and Khan Academy in the area of data science related skills, 2) applying Latent Dirichlet Allocation (LDA) on the collected resources in order to extract existing topics related to these skills, and 3) defining topic distributions covered by a particular OER. To evaluate our model, we used the data-set of educational resources from Youtube, and compared our topic distribution results with their manually defined target topics with the help of 3 experts in the area of data science. As a result, our model extracted topics with 79% of F1-score.
Keywords: Open Educational Resource; OER; Topic Extraction; Text Mining; Machine Learning
Type: bookPart; Text
Publishing status: acceptedVersion
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:Informationswissenschaften

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Molavi, Mohammadreza, Mohammadreza Tavakoli and Gábor Kismihók, 2020. Extracting Topics from Open Educational Resources. In: . Ithaca, NY : Cornell University
Molavi, M., Tavakoli, M. and Kismihók, G. (2020) “Extracting Topics from Open Educational Resources.” Ithaca, NY : Cornell University.
Molavi M, Tavakoli M, Kismihók G. Extracting Topics from Open Educational Resources. Ithaca, NY : Cornell University; 2020.
Molavi, M., Tavakoli, M., & Kismihók, G. (2020). Extracting Topics from Open Educational Resources. Ithaca, NY : Cornell University.
Molavi M, Tavakoli M, Kismihók G. Extracting Topics from Open Educational Resources. In: Ithaca, NY : Cornell University; 2020.


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