Details
| Original language | English |
|---|---|
| Title of host publication | ASSETS 2020 - 22nd International ACM SIGACCESS Conference on Computers and Accessibility |
| ISBN (electronic) | 9781450371032 |
| Publication status | Published - 26 Oct 2020 |
| Externally published | Yes |
| Event | 22nd International ACM SIGACCESS Conference on Computers and Accessibility, ASSETS 2020 - Virtual, Online, Greece Duration: 26 Oct 2020 → 28 Oct 2020 |
Abstract
Open Educational Resources are becoming a significant source of learning that are widely used for various educational purposes and levels. Learners have diverse backgrounds and needs, especially when it comes to learners with accessibility requirements. Persons with disabilities have significantly lower employment rates partly due to the lack of access to education and vocational rehabilitation and training. It is not surprising therefore, that providing high quality OERs that facilitate the self-development towards specific jobs and skills on the labor market in the light of special preferences of learners with disabilities is difficult. In this paper, we introduce a personalized OER recommeder system that considers skills, occupations, and accessibility properties of learners to retrieve the most adequate and high-quality OERs. This is done by: 1) describing the profile of learners with disabilities, 2) collecting and analysing more than 1,500 OERs, 3) filtering OERs based on their accessibility features and predicted quality, and 4) providing personalised OER recommendations for learners according to their accessibility needs. As a result, the OERs retrieved by our method proved to satisfy more accessibility checks than other OERs. Moreover, we evaluated our results with five experts in educating people with visual and cognitive impairments. The evaluation showed that our recommendations are potentially helpful for learners with accessibility needs.
ASJC Scopus subject areas
- Computer Science(all)
- Computer Networks and Communications
- Computer Science(all)
- Hardware and Architecture
- Computer Science(all)
- Human-Computer Interaction
- Computer Science(all)
- Software
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ASSETS 2020 - 22nd International ACM SIGACCESS Conference on Computers and Accessibility. 2020. 3418021.
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - An OER Recommender System Supporting Accessibility Requirements
AU - Elias, Mirette
AU - Tavakoli, Mohammadreza
AU - Lohmann, Steffen
AU - Kismihok, Gabor
AU - Auer, Sören
PY - 2020/10/26
Y1 - 2020/10/26
N2 - Open Educational Resources are becoming a significant source of learning that are widely used for various educational purposes and levels. Learners have diverse backgrounds and needs, especially when it comes to learners with accessibility requirements. Persons with disabilities have significantly lower employment rates partly due to the lack of access to education and vocational rehabilitation and training. It is not surprising therefore, that providing high quality OERs that facilitate the self-development towards specific jobs and skills on the labor market in the light of special preferences of learners with disabilities is difficult. In this paper, we introduce a personalized OER recommeder system that considers skills, occupations, and accessibility properties of learners to retrieve the most adequate and high-quality OERs. This is done by: 1) describing the profile of learners with disabilities, 2) collecting and analysing more than 1,500 OERs, 3) filtering OERs based on their accessibility features and predicted quality, and 4) providing personalised OER recommendations for learners according to their accessibility needs. As a result, the OERs retrieved by our method proved to satisfy more accessibility checks than other OERs. Moreover, we evaluated our results with five experts in educating people with visual and cognitive impairments. The evaluation showed that our recommendations are potentially helpful for learners with accessibility needs.
AB - Open Educational Resources are becoming a significant source of learning that are widely used for various educational purposes and levels. Learners have diverse backgrounds and needs, especially when it comes to learners with accessibility requirements. Persons with disabilities have significantly lower employment rates partly due to the lack of access to education and vocational rehabilitation and training. It is not surprising therefore, that providing high quality OERs that facilitate the self-development towards specific jobs and skills on the labor market in the light of special preferences of learners with disabilities is difficult. In this paper, we introduce a personalized OER recommeder system that considers skills, occupations, and accessibility properties of learners to retrieve the most adequate and high-quality OERs. This is done by: 1) describing the profile of learners with disabilities, 2) collecting and analysing more than 1,500 OERs, 3) filtering OERs based on their accessibility features and predicted quality, and 4) providing personalised OER recommendations for learners according to their accessibility needs. As a result, the OERs retrieved by our method proved to satisfy more accessibility checks than other OERs. Moreover, we evaluated our results with five experts in educating people with visual and cognitive impairments. The evaluation showed that our recommendations are potentially helpful for learners with accessibility needs.
UR - http://www.scopus.com/inward/record.url?scp=85096995676&partnerID=8YFLogxK
U2 - 10.1145/3373625.3418021
DO - 10.1145/3373625.3418021
M3 - Conference contribution
AN - SCOPUS:85096995676
BT - ASSETS 2020 - 22nd International ACM SIGACCESS Conference on Computers and Accessibility
T2 - 22nd International ACM SIGACCESS Conference on Computers and Accessibility, ASSETS 2020
Y2 - 26 October 2020 through 28 October 2020
ER -