  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%.
