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Titel: Labour Market Information Driven, Personalized, OER Recommendation System for Lifelong Learners
Autor(en): Tavakoli, MohammadrezaMol, StefanKismihók, Gábor
Herausgeber: Lane, H. ChadZvacek, SusanUhomoibhi, James
Verlagsversion: https://doi.org/10.5220/0009420300960104
URI: https://oa.tib.eu/renate/handle/123456789/6106
https://doi.org/10.34657/5154
Erscheinungsjahr: 2020
Buch: Proceedings of the 12th International Conference on Computer Supported Education Vol. 2
Startseite: 96
Endseite: 104
Verlag: Setúbal, Portugal : Science and Technology Publications, Lda
Abstract: In this paper, we suggest a novel method to aid lifelong learners to access relevant OER based learning content to master skills demanded on the labour market. Our software prototype 1) applies Text Classification and Text Mining methods on vacancy announcements to decompose jobs into meaningful skills components, which lifelong learners should target; and 2) creates a hybrid OER Recommender System to suggest personalized learning content for learners to progress towards their skill targets. For the first evaluation of this prototype we focused on two job areas: Data Scientist, and Mechanical Engineer. We applied our skill extractor approach and provided OER recommendations for learners targeting these jobs. We conducted in-depth, semi-structured interviews with 12 subject matter experts to learn how our prototype performs in terms of its objectives, logic, and contribution to learning. More than 150 recommendations were generated, and 76.9% of these recommendations were treated as us eful by the interviewees. Interviews revealed that a personalized OER recommender system, based on skills demanded by labour market, has the potential to improve the learning experience of lifelong learners.
Schlagwörter: Lifelong Learning; Open Education Resources; Recommender Systems; Labour Market Intelligence; Machine Learning; Text Mining
Publikationstyp: bookPart; Text
Publikationsstatus: publishedVersion
DDC: 020
Lizenz: CC BY-NC-ND 4.0 Unported
Link zur Lizenz: https://creativecommons.org/licenses/by-nc-nd/4.0/
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Tavakoli, Mohammadreza, Stefan Mol and Gábor Kismihók, 2020. Labour Market Information Driven, Personalized, OER Recommendation System for Lifelong Learners. In: (Hrsg.)H. Chad Lane, Susan Zvacek and James Uhomoibhi. Setúbal, Portugal : Science and Technology Publications, Lda. ISBN 978-989-758-417-6
Tavakoli, M., Mol, S. and Kismihók, G. (2020) “Labour Market Information Driven, Personalized, OER Recommendation System for Lifelong Learners.” Setúbal, Portugal : Science and Technology Publications, Lda. doi: https://doi.org/10.5220/0009420300960104.
Tavakoli M, Mol S, Kismihók G. Labour Market Information Driven, Personalized, OER Recommendation System for Lifelong Learners. In: , editorLane H C, Zvacek S, Uhomoibhi J. Setúbal, Portugal : Science and Technology Publications, Lda; 2020.
Tavakoli, M., Mol, S., & Kismihók, G. (2020). Labour Market Information Driven, Personalized, OER Recommendation System for Lifelong Learners. Setúbal, Portugal : Science and Technology Publications, Lda. https://doi.org/https://doi.org/10.5220/0009420300960104
Tavakoli M, Mol S, Kismihók G. Labour Market Information Driven, Personalized, OER Recommendation System for Lifelong Learners. In: , ed.Lane H C, Zvacek S, Uhomoibhi J Setúbal, Portugal : Science and Technology Publications, Lda; 2020. doi:https://doi.org/10.5220/0009420300960104


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