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Extracting topics from open educational resources

Publikation: Beitrag in Buch/Bericht/Sammelwerk/KonferenzbandAufsatz in KonferenzbandForschungPeer-Review

Autorschaft

  • Mohammadreza Molavi
  • Mohammadreza Tavakoli
  • Gábor Kismihók

Externe Organisationen

  • Amirkabir University of Technology
  • Technische Informationsbibliothek (TIB) Leibniz-Informationszentrum Technik und Naturwissenschaften und Universitätsbibliothek

Details

OriginalspracheEnglisch
Titel des SammelwerksAddressing Global Challenges and Quality Education - 15th European Conference on Technology Enhanced Learning, EC-TEL 2020, Proceedings
Herausgeber/-innenCarlos Alario-Hoyos, María Jesús Rodríguez-Triana, Maren Scheffel, Inmaculada Arnedillo-Sánchez, Sebastian Maximilian Dennerlein
Herausgeber (Verlag)Springer Science and Business Media Deutschland GmbH
Seiten455-460
Seitenumfang6
ISBN (Print)9783030577162
PublikationsstatusVeröffentlicht - 2020
Extern publiziertJa
Veranstaltung15th European Conference on Technology Enhanced Learning, EC-TEL 2020 - Heidelberg, Deutschland
Dauer: 14 Sept. 202018 Sept. 2020

Publikationsreihe

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Band12315 LNCS
ISSN (Print)0302-9743
ISSN (elektronisch)1611-3349

Abstract

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, OER based 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 27 lectures from Coursera and Khan Academy in the area of an important skill in the area of Data Science (i.e. Text Mining as our first focus), 2) applying Latent Dirichlet Allocation (LDA) on the collected resources in order to extract existing topics related to the skill, 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 76% of F1-score.

ASJC Scopus Sachgebiete

Zitieren

Extracting topics from open educational resources. / Molavi, Mohammadreza; Tavakoli, Mohammadreza; Kismihók, Gábor.
Addressing Global Challenges and Quality Education - 15th European Conference on Technology Enhanced Learning, EC-TEL 2020, Proceedings. Hrsg. / Carlos Alario-Hoyos; María Jesús Rodríguez-Triana; Maren Scheffel; Inmaculada Arnedillo-Sánchez; Sebastian Maximilian Dennerlein. Springer Science and Business Media Deutschland GmbH, 2020. S. 455-460 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Band 12315 LNCS).

Publikation: Beitrag in Buch/Bericht/Sammelwerk/KonferenzbandAufsatz in KonferenzbandForschungPeer-Review

Molavi, M, Tavakoli, M & Kismihók, G 2020, Extracting topics from open educational resources. in C Alario-Hoyos, MJ Rodríguez-Triana, M Scheffel, I Arnedillo-Sánchez & SM Dennerlein (Hrsg.), Addressing Global Challenges and Quality Education - 15th European Conference on Technology Enhanced Learning, EC-TEL 2020, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Bd. 12315 LNCS, Springer Science and Business Media Deutschland GmbH, S. 455-460, 15th European Conference on Technology Enhanced Learning, EC-TEL 2020, Heidelberg, Deutschland, 14 Sept. 2020. https://doi.org/10.48550/arXiv.2006.11109, https://doi.org/10.1007/978-3-030-57717-9_44
Molavi, M., Tavakoli, M., & Kismihók, G. (2020). Extracting topics from open educational resources. In C. Alario-Hoyos, M. J. Rodríguez-Triana, M. Scheffel, I. Arnedillo-Sánchez, & S. M. Dennerlein (Hrsg.), Addressing Global Challenges and Quality Education - 15th European Conference on Technology Enhanced Learning, EC-TEL 2020, Proceedings (S. 455-460). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Band 12315 LNCS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.48550/arXiv.2006.11109, https://doi.org/10.1007/978-3-030-57717-9_44
Molavi M, Tavakoli M, Kismihók G. Extracting topics from open educational resources. in Alario-Hoyos C, Rodríguez-Triana MJ, Scheffel M, Arnedillo-Sánchez I, Dennerlein SM, Hrsg., Addressing Global Challenges and Quality Education - 15th European Conference on Technology Enhanced Learning, EC-TEL 2020, Proceedings. Springer Science and Business Media Deutschland GmbH. 2020. S. 455-460. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). Epub 2020 Sep 7. doi: 10.48550/arXiv.2006.11109, 10.1007/978-3-030-57717-9_44
Molavi, Mohammadreza ; Tavakoli, Mohammadreza ; Kismihók, Gábor. / Extracting topics from open educational resources. Addressing Global Challenges and Quality Education - 15th European Conference on Technology Enhanced Learning, EC-TEL 2020, Proceedings. Hrsg. / Carlos Alario-Hoyos ; María Jesús Rodríguez-Triana ; Maren Scheffel ; Inmaculada Arnedillo-Sánchez ; Sebastian Maximilian Dennerlein. Springer Science and Business Media Deutschland GmbH, 2020. S. 455-460 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
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AU - Molavi, Mohammadreza

AU - Tavakoli, Mohammadreza

AU - Kismihók, Gábor

PY - 2020

Y1 - 2020

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