Please use this identifier to cite or link to this item: https://oa.tib.eu/renate/handle/123456789/6106
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dc.rights.licenseCC BY-NC-ND 4.0 Unportedeng
dc.contributor.authorTavakoli, Mohammadreza-
dc.contributor.authorMol, Stefan-
dc.contributor.authorKismihók, Gábor-
dc.contributor.editorLane, H. Chad-
dc.contributor.editorZvacek, Susan-
dc.contributor.editorUhomoibhi, James-
dc.date.accessioned2021-03-23T08:29:09Z-
dc.date.available2021-03-23T08:29:09Z-
dc.date.issued2020-
dc.identifier.urihttps://oa.tib.eu/renate/handle/123456789/6106-
dc.identifier.urihttps://doi.org/10.34657/5154-
dc.description.abstractIn 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.eng
dc.language.isoengeng
dc.publisherSetúbal, Portugal : Science and Technology Publications, Ldaeng
dc.relation.ispartofProceedings of the 12th International Conference on Computer Supported Education Vol. 2eng
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/eng
dc.subjectLifelong Learningeng
dc.subjectOpen Education Resourceseng
dc.subjectRecommender Systemseng
dc.subjectLabour Market Intelligenceeng
dc.subjectMachine Learningeng
dc.subjectText Miningeng
dc.subject.classificationKonferenzschriftger
dc.subject.ddc020eng
dc.titleLabour Market Information Driven, Personalized, OER Recommendation System for Lifelong Learnerseng
dc.typebookParteng
dc.typeTexteng
dc.description.versionpublishedVersioneng
wgl.contributorTIBeng
wgl.subjectInformatikeng
wgl.typeBuchkapitel / Sammelwerksbeitrageng
wgl.typeKonferenzbeitrageng
dc.bibliographicCitation.firstPage96eng
dc.bibliographicCitation.lastPage104eng
dc.relation.doihttps://doi.org/10.5220/0009420300960104-
dc.relation.isbn978-989-758-417-6-
tib.accessRightsopenAccesseng
tib.relation.conferenceCSEDU 2020 - 12th International Conference on Computer Supported Education, 2-4 May 2020, onlineeng
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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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