Metadata Analysis of Open Educational Resources

TL;DR

This paper analyzes the metadata of nearly 9,000 open educational resources to develop models that predict their quality, demonstrating high accuracy and applicability across different educational repositories.

Contribution

It introduces metadata-based scoring and prediction models for assessing OER quality, leveraging large-scale metadata analysis for improved search and quality control.

Findings

High-quality OERs can be detected with 94.6% accuracy

Metadata quality closely correlates with content quality

Models are applicable to other educational repositories like YouTube

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.