Understanding Learning Resources Metadata for Primary and Secondary Education

Abstract

Educational resources can be easily found on the Web; most search engines base their algorithms on the resources text or popularity. Teachers must navigate the results until they find an appropriate resource making search a tedious and cumbersome task. Specialized repositories contain resources that are annotated with metadata that aims to facilitate the discovery of quality resources. Nevertheless, content abundance and variety makes searching a complex task. Recommender systems can assist teachers in finding the proper content either by determining clusters of similar users, and inferring the user interest on a resource; clusters of similar resources, or a mix of both. Probabilistic model based (PMB) techniques, on the other hand, make possible to classify resources into more than one cluster with diverse probability degree. In this paper we use recommender systems and PMB techniques to analyze a dataset produced by primary and secondary level teachers during four years and under natural conditions. We found that a hybrid recommendation, the CTR technique, performs better than other approaches despite the high sparseness of the dataset. In addition, learning resources annotated with curriculum metadata had a positive impact on recommenders accuracy whereas free-text or other metadata negatively impacted the results.