Conference

Learning object recommendation system evaluation

This paper evaluates the effectiveness of a learning object recommendation system in enhancing the discovery of educational resources. Learning objects, defined as digital content designed for teaching, are stored in repositories equipped with indexing and retrieval mechanisms. The study applies a recommender algorithm to a repository at the Federal Institute of Rio Grande do Sul and analyzes its impact on user experience. The findings demonstrate how recommender systems can assist users in efficiently locating relevant learning objects, improving accessibility and usability within educational repositories.