A Recommender System that Allows Reasoning and Interoperability over Educational Content Metadata

This work presents a recommender system infrastructure for educational material described with metadata. The idea is to provide a set of flexible premises that allows reasoning and improved personalized results in learning content recommendations. For its operation we propose the use of the OBAA standard, which is an extension of IEEE LOM that provides interoperability among hardware platforms contextualized with the Brazilian Educational domain. The technological core of this infrastructure is based on the use of FOAF to describe user profiles, an OWL Ontology to describe specific domain features as well as to facilitate the reasoning process, a Web Service that connects to a federate educational content repository, and a Collaborative Filtering Algorithm as an algorithm recommendation.