Details
| Originalsprache | Englisch |
|---|---|
| Titel des Sammelwerks | Proceedings - IEEE 20th International Conference on Advanced Learning Technologies, ICALT 2020 |
| Herausgeber/-innen | Maiga Chang, Demetrios G Sampson, Ronghuai Huang, Danial Hooshyar, Nian-Shing Chen, Kinshuk Kinshuk, Margus Pedaste |
| Herausgeber (Verlag) | Institute of Electrical and Electronics Engineers Inc. |
| Seiten | 29-31 |
| Seitenumfang | 3 |
| ISBN (Print) | 9781728160900 |
| Publikationsstatus | Veröffentlicht - 2020 |
| Extern publiziert | Ja |
| Veranstaltung | 20th IEEE International Conference on Advanced Learning Technologies, ICALT 2020 - Virtual, Online, Estland Dauer: 6 Juli 2020 → 9 Juli 2020 |
Publikationsreihe
| Name | Proceedings - International Conference on Advanced Learning Technologies (ICALT) |
|---|---|
| ISSN (Print) | 2161-377X |
Abstract
In the recent decade, online learning environments have accumulated millions of Open Educational Resources (OERs). However, for learners, finding relevant and high quality OERs is a complicated and time-consuming activity. Furthermore, metadata play a key role in offering high quality services such as recommendation and search. Metadata can also be used for automatic OER quality control as, in the light of the continuously increasing number of OERs, manual quality control is getting more and more difficult. In this work, we collected the metadata of 8,887 OERs to perform an exploratory data analysis to observe the effect of quality control on metadata quality. Subsequently, we propose an OER metadata scoring model, and build a metadata-based prediction model to anticipate the quality of OERs. Based on our data and model, we were able to detect high-quality OERs with the F1 score of 94.6%.
ASJC Scopus Sachgebiete
- Informatik (insg.)
- Computernetzwerke und -kommunikation
- Informatik (insg.)
- Angewandte Informatik
- Ingenieurwesen (insg.)
- Sicherheit, Risiko, Zuverlässigkeit und Qualität
- Ingenieurwesen (insg.)
- Medientechnik
- Sozialwissenschaften (insg.)
- Ausbildung bzw. Denomination
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- BibTex
- RIS
Proceedings - IEEE 20th International Conference on Advanced Learning Technologies, ICALT 2020. Hrsg. / Maiga Chang; Demetrios G Sampson; Ronghuai Huang; Danial Hooshyar; Nian-Shing Chen; Kinshuk Kinshuk; Margus Pedaste. Institute of Electrical and Electronics Engineers Inc., 2020. S. 29-31 9155928 (Proceedings - International Conference on Advanced Learning Technologies (ICALT)).
Publikation: Beitrag in Buch/Bericht/Sammelwerk/Konferenzband › Aufsatz in Konferenzband › Forschung › Peer-Review
}
TY - GEN
T1 - Quality Prediction of Open Educational Resources
T2 - 20th IEEE International Conference on Advanced Learning Technologies, ICALT 2020
AU - Tavakoli, Mohammadreza
AU - Elias, Mirette
AU - Kismihok, Gabor
AU - Auer, Soren
PY - 2020
Y1 - 2020
N2 - In the recent decade, online learning environments have accumulated millions of Open Educational Resources (OERs). However, for learners, finding relevant and high quality OERs is a complicated and time-consuming activity. Furthermore, metadata play a key role in offering high quality services such as recommendation and search. Metadata can also be used for automatic OER quality control as, in the light of the continuously increasing number of OERs, manual quality control is getting more and more difficult. In this work, we collected the metadata of 8,887 OERs to perform an exploratory data analysis to observe the effect of quality control on metadata quality. Subsequently, we propose an OER metadata scoring model, and build a metadata-based prediction model to anticipate the quality of OERs. Based on our data and model, we were able to detect high-quality OERs with the F1 score of 94.6%.
AB - In the recent decade, online learning environments have accumulated millions of Open Educational Resources (OERs). However, for learners, finding relevant and high quality OERs is a complicated and time-consuming activity. Furthermore, metadata play a key role in offering high quality services such as recommendation and search. Metadata can also be used for automatic OER quality control as, in the light of the continuously increasing number of OERs, manual quality control is getting more and more difficult. In this work, we collected the metadata of 8,887 OERs to perform an exploratory data analysis to observe the effect of quality control on metadata quality. Subsequently, we propose an OER metadata scoring model, and build a metadata-based prediction model to anticipate the quality of OERs. Based on our data and model, we were able to detect high-quality OERs with the F1 score of 94.6%.
KW - Big data
KW - Data analysis
KW - Metadata quality
KW - OER
KW - OER quality
KW - Open educational resources
KW - Quality prediction
UR - http://www.scopus.com/inward/record.url?scp=85091133480&partnerID=8YFLogxK
U2 - 10.1109/ICALT49669.2020.00007
DO - 10.1109/ICALT49669.2020.00007
M3 - Conference contribution
AN - SCOPUS:85091133480
SN - 9781728160900
T3 - Proceedings - International Conference on Advanced Learning Technologies (ICALT)
SP - 29
EP - 31
BT - Proceedings - IEEE 20th International Conference on Advanced Learning Technologies, ICALT 2020
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.
Y2 - 6 July 2020 through 9 July 2020
ER -