Details
| Original language | English |
|---|---|
| Title of host publication | 2020 IEEE 20th International Conference on Advanced Learning Technologies (ICALT) |
| Editors | Maiga Chang, Demetrios G Sampson, Ronghuai Huang, Danial Hooshyar, Nian-Shing Chen, Kinshuk Kinshuk, Margus Pedaste |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Number of pages | 5 |
| ISBN (electronic) | 9781728160900 |
| Publication status | Published - Jul 2020 |
| Externally published | Yes |
| Event | 20th IEEE International Conference on Advanced Learning Technologies, ICALT 2020 - Virtual, Online, Estonia Duration: 6 Jul 2020 → 9 Jul 2020 |
Abstract
In this paper, we suggest a novel method to help learners find relevant open educational videos to master skills demanded on the labour market. We have built a prototype, which 1) applies text classification and text mining methods on job vacancy announcements to match jobs and their required skills; 2) predicts the quality of videos; and 3) creates an open educational video recommender system to suggest personalized learning content to learners. For the first evaluation of this prototype we focused on the area of data science related jobs. Our prototype was evaluated by in-depth, semi-structured interviews. 15 subject matter experts provided feedback to assess how our recommender prototype performs in terms of its objectives, logic, and contribution to learning. More than 250 videos were recommended, and 82.8% of these recommendations were treated as useful by the interviewees. Moreover, interviews revealed that our personalized video recommender system, has the potential to improve the learning experience.
Keywords
- Educational recommender system, Lifelong learning, Machine learning, OER, Open educational resource, Text classification, Text mining, Video recommender system
ASJC Scopus subject areas
- Computer Science(all)
- Computer Networks and Communications
- Computer Science(all)
- Computer Science Applications
- Engineering(all)
- Safety, Risk, Reliability and Quality
- Engineering(all)
- Media Technology
- Social Sciences(all)
- Education
Cite this
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- BibTeX
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2020 IEEE 20th International Conference on Advanced Learning Technologies (ICALT). ed. / Maiga Chang; Demetrios G Sampson; Ronghuai Huang; Danial Hooshyar; Nian-Shing Chen; Kinshuk Kinshuk; Margus Pedaste. Institute of Electrical and Electronics Engineers Inc., 2020. 9155881.
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - A recommender system for open educational videos based on skill requirements
AU - Tavakoli, Mohammadreza
AU - Hakimov, Sherzod
AU - Ewerth, Ralph
AU - Kismihok, Gabor
N1 - Funding information: Part of this work is financially supported by the Leibniz Association, Germany (Leibniz Competition 2018, funding line “Collaborative Excellence”, Project SALIENT [K68/2017]).
PY - 2020/7
Y1 - 2020/7
N2 - In this paper, we suggest a novel method to help learners find relevant open educational videos to master skills demanded on the labour market. We have built a prototype, which 1) applies text classification and text mining methods on job vacancy announcements to match jobs and their required skills; 2) predicts the quality of videos; and 3) creates an open educational video recommender system to suggest personalized learning content to learners. For the first evaluation of this prototype we focused on the area of data science related jobs. Our prototype was evaluated by in-depth, semi-structured interviews. 15 subject matter experts provided feedback to assess how our recommender prototype performs in terms of its objectives, logic, and contribution to learning. More than 250 videos were recommended, and 82.8% of these recommendations were treated as useful by the interviewees. Moreover, interviews revealed that our personalized video recommender system, has the potential to improve the learning experience.
AB - In this paper, we suggest a novel method to help learners find relevant open educational videos to master skills demanded on the labour market. We have built a prototype, which 1) applies text classification and text mining methods on job vacancy announcements to match jobs and their required skills; 2) predicts the quality of videos; and 3) creates an open educational video recommender system to suggest personalized learning content to learners. For the first evaluation of this prototype we focused on the area of data science related jobs. Our prototype was evaluated by in-depth, semi-structured interviews. 15 subject matter experts provided feedback to assess how our recommender prototype performs in terms of its objectives, logic, and contribution to learning. More than 250 videos were recommended, and 82.8% of these recommendations were treated as useful by the interviewees. Moreover, interviews revealed that our personalized video recommender system, has the potential to improve the learning experience.
KW - Educational recommender system
KW - Lifelong learning
KW - Machine learning
KW - OER
KW - Open educational resource
KW - Text classification
KW - Text mining
KW - Video recommender system
UR - http://www.scopus.com/inward/record.url?scp=85091175693&partnerID=8YFLogxK
U2 - 10.48550/arXiv.2005.10595
DO - 10.48550/arXiv.2005.10595
M3 - Conference contribution
AN - SCOPUS:85091175693
BT - 2020 IEEE 20th International Conference on Advanced Learning Technologies (ICALT)
A2 - Chang, Maiga
A2 - Sampson, Demetrios G
A2 - Huang, Ronghuai
A2 - Hooshyar, Danial
A2 - Chen, Nian-Shing
A2 - Kinshuk, Kinshuk
A2 - Pedaste, Margus
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 20th IEEE International Conference on Advanced Learning Technologies, ICALT 2020
Y2 - 6 July 2020 through 9 July 2020
ER -