Applying the FAIR Principles to Open Educational Resources: A Semantic Similarity Approach to Improve Resource Discovery

Open educational resources (OER) are teaching, learning, or research resources freely available for use and reuse. Despite their potential, OER uptake in existing education systems remains low, primarily due to challenges in locating suitable resources. This study addresses this challenge by proposing and implementing a workflow applying the FAIR (Findable, Accessible, Interoperable, and Reusable) principles to OER. We demonstrated this framework within Earth system sciences as an application domain. We constructed a knowledge graph of approximately 500 FAIR OER, each annotated with structured metadata using Schema.org vocabulary and made accessible through a SPARQL endpoint. To bridge the gap between making resources queryable and enabling their practical reuse, we employed a transformer-based language model (Sentence-BERT). The model was fine-tuned using few-shot learning on a domain-specific dataset of course-description pairs. This specialized model was then used to map the OER collection against over 200 university courses across five academic programs at a German university, based on semantic similarity between OER descriptions and university course descriptions. Expert evaluation of the model’s recommendations demonstrated 74% accuracy in identifying reusable OER for university courses. Notably, even with limited training data, fine-tuning the Sentence-BERT model significantly improved performance, resulting in a 16% reduction in mean squared error compared to the base model. This study provides both a generalizable methodology and a practical demonstration of how FAIR principles can streamline OER discovery, potentially accelerating OER uptake in higher education.