Details
| Original language | English |
|---|---|
| Title of host publication | CSEDU 2020 - Proceedings of the 12th International Conference on Computer Supported Education |
| Editors | H. Chad Lane, Susan Zvacek, James Uhomoibhi |
| Pages | 96-104 |
| Number of pages | 9 |
| ISBN (electronic) | 9789897584176 |
| Publication status | Published - 2020 |
| Externally published | Yes |
| Event | 12th International Conference on Computer Supported Education, CSEDU 2020 - Virtual, Online Duration: 2 May 2020 → 4 May 2020 |
Publication series
| Name | CSEDU 2020 - Proceedings of the 12th International Conference on Computer Supported Education |
|---|---|
| Volume | 2 |
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.
Keywords
- Labour Market Intelligence, Lifelong Learning, Machine Learning, Open Education Resources, Recommender Systems, Text Mining
ASJC Scopus subject areas
- Computer Science(all)
- Information Systems
- Computer Science(all)
- Computer Science Applications
- Social Sciences(all)
- Education
Cite this
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- BibTeX
- RIS
CSEDU 2020 - Proceedings of the 12th International Conference on Computer Supported Education. ed. / H. Chad Lane; Susan Zvacek; James Uhomoibhi. 2020. p. 96-104 (CSEDU 2020 - Proceedings of the 12th International Conference on Computer Supported Education; Vol. 2).
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - Labour market information driven, personalized, oer recommendation system for lifelong learners
AU - Tavakoli, Mohammadreza
AU - Mol, Stefan T.
AU - Kismihók, Gábor
PY - 2020
Y1 - 2020
N2 - 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.
AB - 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.
KW - Labour Market Intelligence
KW - Lifelong Learning
KW - Machine Learning
KW - Open Education Resources
KW - Recommender Systems
KW - Text Mining
UR - http://www.scopus.com/inward/record.url?scp=85091752092&partnerID=8YFLogxK
U2 - 10.5220/0009420300960104
DO - 10.5220/0009420300960104
M3 - Conference contribution
AN - SCOPUS:85091752092
T3 - CSEDU 2020 - Proceedings of the 12th International Conference on Computer Supported Education
SP - 96
EP - 104
BT - CSEDU 2020 - Proceedings of the 12th International Conference on Computer Supported Education
A2 - Lane, H. Chad
A2 - Zvacek, Susan
A2 - Uhomoibhi, James
T2 - 12th International Conference on Computer Supported Education, CSEDU 2020
Y2 - 2 May 2020 through 4 May 2020
ER -