  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.
