Modelo multi-agente para recomendación híbrida de objetos de aprendizaje Multi-agent Model for Hybrid Recommendation of Learning Objects Modèle multi-agent pour recommandation hybride d'objets d'apprentissage

Learning Objects (LO) are distinguished from traditional educational resources due to their availability through repositories. The creation of federations of learning objects (LO) repositories is a major global trend that aims at reusing LO in order to support teaching–learning processes. Considering the difficulty of delivering educational resources that have particular characteristics, we propose a multi-agent approach to help identifying and recommending LO based on the user's profile. Several techniques have already been proposed to make recommendations, when these techniques are combined the recommendation is called hybrid. This paper proposes a multiagent model in order to deliver tailored and adaptive results. According to the precision calculations which were performed we conclude that the objects delivered by the system are relevant to the student.