Labour Market Information Driven, Personalized, OER Recommendation System for Lifelong Learners

Mohammadreza Tavakoli, Stefan Mol, Gábor Kismihók

2020

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 useful 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.

Paper Citation

in Harvard Style

Tavakoli M., Mol S. and Kismihók G. (2020). Labour Market Information Driven, Personalized, OER Recommendation System for Lifelong Learners.In Proceedings of the 12th International Conference on Computer Supported Education - Volume 2: CSEDU, ISBN 978-989-758-417-6, pages 96-104. DOI: 10.5220/0009420300960104

in Bibtex Style

@conference{csedu20,
author={Mohammadreza Tavakoli and Stefan Mol and Gábor Kismihók},
title={Labour Market Information Driven, Personalized, OER Recommendation System for Lifelong Learners},
booktitle={Proceedings of the 12th International Conference on Computer Supported Education - Volume 2: CSEDU,},
year={2020},
pages={96-104},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0009420300960104},
isbn={978-989-758-417-6},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 12th International Conference on Computer Supported Education - Volume 2: CSEDU,
TI - Labour Market Information Driven, Personalized, OER Recommendation System for Lifelong Learners
SN - 978-989-758-417-6
AU - Tavakoli M.
AU - Mol S.
AU - Kismihók G.
PY - 2020
SP - 96
EP - 104
DO - 10.5220/0009420300960104