Details
| Original language | English |
|---|---|
| Title of host publication | LAK21: 11th International Learning Analytics and Knowledge Conference |
| Place of Publication | New York |
| Publisher | Association for Computing Machinery (ACM) |
| Pages | 626-631 |
| Number of pages | 6 |
| ISBN (electronic) | 9781450389358 |
| Publication status | Published - 12 Apr 2021 |
| Externally published | Yes |
| Event | 11th International Conference on Learning Analytics and Knowledge: The Impact we Make: The Contributions of Learning Analytics to Learning, LAK 2021 - Virtual, Online, United States Duration: 12 Apr 2021 → 16 Apr 2021 |
Publication series
| Name | ACM International Conference Proceeding Series |
|---|
Abstract
Open Educational Resources (OERs) are openly licensed educational materials that are widely used for learning. Nowadays, many online learning repositories provide millions of OERs. Therefore, it is exceedingly difficult for learners to find the most appropriate OER among these resources. Subsequently, the precise OER metadata is critical for providing high-quality services such as search and recommendation. Moreover, metadata facilitates the process of automatic OER quality control as the continuously increasing number of OERs makes manual quality control extremely difficult. This work uses the metadata of 8,887 OERs to perform an exploratory data analysis on OER metadata. Accordingly, this work proposes metadata-based scoring and prediction models to anticipate the quality of OERs. Based on the results, our analysis demonstrated that OER metadata and OER content qualities are closely related, as we could detect high-quality OERs with an accuracy of 94.6%. Our model was also evaluated on 884 educational videos from Youtube to show its applicability on other educational repositories.
Keywords
- Exploratory analysis, Machine learning, Metadata analysis, OER, Open educational resources, Prediction models
ASJC Scopus subject areas
- Computer Science(all)
- Software
- Computer Science(all)
- Human-Computer Interaction
- Computer Science(all)
- Computer Vision and Pattern Recognition
- Computer Science(all)
- Computer Networks and Communications
Cite this
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LAK21: 11th International Learning Analytics and Knowledge Conference. New York: Association for Computing Machinery (ACM), 2021. p. 626-631 (ACM International Conference Proceeding Series).
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - Metadata analysis of open educational resources
AU - Tavakoli, Mohammadreza
AU - Elias, Mirette
AU - Kismihók, Gábor
AU - Auer, Sören
PY - 2021/4/12
Y1 - 2021/4/12
N2 - Open Educational Resources (OERs) are openly licensed educational materials that are widely used for learning. Nowadays, many online learning repositories provide millions of OERs. Therefore, it is exceedingly difficult for learners to find the most appropriate OER among these resources. Subsequently, the precise OER metadata is critical for providing high-quality services such as search and recommendation. Moreover, metadata facilitates the process of automatic OER quality control as the continuously increasing number of OERs makes manual quality control extremely difficult. This work uses the metadata of 8,887 OERs to perform an exploratory data analysis on OER metadata. Accordingly, this work proposes metadata-based scoring and prediction models to anticipate the quality of OERs. Based on the results, our analysis demonstrated that OER metadata and OER content qualities are closely related, as we could detect high-quality OERs with an accuracy of 94.6%. Our model was also evaluated on 884 educational videos from Youtube to show its applicability on other educational repositories.
AB - Open Educational Resources (OERs) are openly licensed educational materials that are widely used for learning. Nowadays, many online learning repositories provide millions of OERs. Therefore, it is exceedingly difficult for learners to find the most appropriate OER among these resources. Subsequently, the precise OER metadata is critical for providing high-quality services such as search and recommendation. Moreover, metadata facilitates the process of automatic OER quality control as the continuously increasing number of OERs makes manual quality control extremely difficult. This work uses the metadata of 8,887 OERs to perform an exploratory data analysis on OER metadata. Accordingly, this work proposes metadata-based scoring and prediction models to anticipate the quality of OERs. Based on the results, our analysis demonstrated that OER metadata and OER content qualities are closely related, as we could detect high-quality OERs with an accuracy of 94.6%. Our model was also evaluated on 884 educational videos from Youtube to show its applicability on other educational repositories.
KW - Exploratory analysis
KW - Machine learning
KW - Metadata analysis
KW - OER
KW - Open educational resources
KW - Prediction models
UR - http://www.scopus.com/inward/record.url?scp=85103885291&partnerID=8YFLogxK
U2 - 10.1145/3448139.3448208
DO - 10.1145/3448139.3448208
M3 - Conference contribution
AN - SCOPUS:85103885291
T3 - ACM International Conference Proceeding Series
SP - 626
EP - 631
BT - LAK21: 11th International Learning Analytics and Knowledge Conference
PB - Association for Computing Machinery (ACM)
CY - New York
T2 - 11th International Conference on Learning Analytics and Knowledge: The Impact we Make: The Contributions of Learning Analytics to Learning, LAK 2021
Y2 - 12 April 2021 through 16 April 2021
ER -