Details
| Original language | English |
|---|---|
| Title of host publication | 2020 IEEE Frontiers in Education Conference, FIE 2020 - Proceedings |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| ISBN (electronic) | 9781728189611 |
| Publication status | Published - 21 Oct 2020 |
| Externally published | Yes |
| Event | 2020 IEEE Frontiers in Education Conference, FIE 2020 - Uppsala, Sweden Duration: 21 Oct 2020 → 24 Oct 2020 |
Publication series
| Name | Proceedings - Frontiers in Education Conference, FIE |
|---|---|
| Volume | 2020-October |
| ISSN (Print) | 1539-4565 |
Abstract
This Work in Progress Research paper departs from the recent, turbulent changes in global societies, forcing many citizens to re-skill themselves to (re)gain employment. Learners therefore need to be equipped with skills to be autonomous and strategic about their own skill development. Subsequently, high-quality, on-line, personalized educational content and services are also essential to serve this high demand for learning content. Open Educational Resources (OERs) have high potential to contribute to the mitigation of these problems, as they are available in a wide range of learning and occupational contexts globally. However, their applicability has been limited, due to low metadata quality and complex quality control. These issues resulted in a lack of personalised OER functions, like recommendation and search. Therefore, we suggest a novel, personalised OER recommendation method to match skill development targets with open learning content. This is done by: 1) using an OER quality prediction model based on metadata, OER properties, and content; 2) supporting learners to set individual skill targets based on actual labour market information, and 3) building a personalized OER recommender to help learners to master their skill targets. Accordingly, we built a prototype focusing on Data Science related jobs, and evaluated this prototype with 23 data scientists in different expertise levels. Pilot participants used our prototype for at least 30 minutes and commented on each of the recommended OERs. As a result, more than 400 recommendations were generated and 80.9% of the recommendations were reported as useful.
Keywords
- educational recommender system, labour market intelligence, lifelong learning, machine learning, OER, Open Educational Resource, text mining
ASJC Scopus subject areas
- Computer Science(all)
- Software
- Social Sciences(all)
- Education
- Computer Science(all)
- Computer Science Applications
Cite this
- Standard
- Harvard
- Apa
- Vancouver
- BibTeX
- RIS
2020 IEEE Frontiers in Education Conference, FIE 2020 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2020. 9274175 (Proceedings - Frontiers in Education Conference, FIE; Vol. 2020-October).
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - OER Recommendations to Support Career Development
AU - Tavakoli, Mohammadreza
AU - Faraji, Ali
AU - Mol, Stefan T.
AU - Kismihok, Gabor
PY - 2020/10/21
Y1 - 2020/10/21
N2 - This Work in Progress Research paper departs from the recent, turbulent changes in global societies, forcing many citizens to re-skill themselves to (re)gain employment. Learners therefore need to be equipped with skills to be autonomous and strategic about their own skill development. Subsequently, high-quality, on-line, personalized educational content and services are also essential to serve this high demand for learning content. Open Educational Resources (OERs) have high potential to contribute to the mitigation of these problems, as they are available in a wide range of learning and occupational contexts globally. However, their applicability has been limited, due to low metadata quality and complex quality control. These issues resulted in a lack of personalised OER functions, like recommendation and search. Therefore, we suggest a novel, personalised OER recommendation method to match skill development targets with open learning content. This is done by: 1) using an OER quality prediction model based on metadata, OER properties, and content; 2) supporting learners to set individual skill targets based on actual labour market information, and 3) building a personalized OER recommender to help learners to master their skill targets. Accordingly, we built a prototype focusing on Data Science related jobs, and evaluated this prototype with 23 data scientists in different expertise levels. Pilot participants used our prototype for at least 30 minutes and commented on each of the recommended OERs. As a result, more than 400 recommendations were generated and 80.9% of the recommendations were reported as useful.
AB - This Work in Progress Research paper departs from the recent, turbulent changes in global societies, forcing many citizens to re-skill themselves to (re)gain employment. Learners therefore need to be equipped with skills to be autonomous and strategic about their own skill development. Subsequently, high-quality, on-line, personalized educational content and services are also essential to serve this high demand for learning content. Open Educational Resources (OERs) have high potential to contribute to the mitigation of these problems, as they are available in a wide range of learning and occupational contexts globally. However, their applicability has been limited, due to low metadata quality and complex quality control. These issues resulted in a lack of personalised OER functions, like recommendation and search. Therefore, we suggest a novel, personalised OER recommendation method to match skill development targets with open learning content. This is done by: 1) using an OER quality prediction model based on metadata, OER properties, and content; 2) supporting learners to set individual skill targets based on actual labour market information, and 3) building a personalized OER recommender to help learners to master their skill targets. Accordingly, we built a prototype focusing on Data Science related jobs, and evaluated this prototype with 23 data scientists in different expertise levels. Pilot participants used our prototype for at least 30 minutes and commented on each of the recommended OERs. As a result, more than 400 recommendations were generated and 80.9% of the recommendations were reported as useful.
KW - educational recommender system
KW - labour market intelligence
KW - lifelong learning
KW - machine learning
KW - OER
KW - Open Educational Resource
KW - text mining
UR - http://www.scopus.com/inward/record.url?scp=85098561976&partnerID=8YFLogxK
U2 - 10.1109/FIE44824.2020.9274175
DO - 10.1109/FIE44824.2020.9274175
M3 - Conference contribution
AN - SCOPUS:85098561976
T3 - Proceedings - Frontiers in Education Conference, FIE
BT - 2020 IEEE Frontiers in Education Conference, FIE 2020 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2020 IEEE Frontiers in Education Conference, FIE 2020
Y2 - 21 October 2020 through 24 October 2020
ER -