BROAD-RS: arquitetura para recomendação de objetos de aprendizagem sensível ao contexto usando agentes e ontologia

Abstract

Learning objects are any resources that might help in the teaching and learning process and to be reused in different contexts. The reuse of learning objects shows its potential to accelerate the preparation and composition of on line courses. A recommendation system in education aims to identify student profile so that it can suggest learning objects suitable to his/her preference. However, when considering content reuse, it is also necessary to adapt them. Context-aware applications are able to modify their behavior based on the users´ informations. One motivation of using a context-sensitive system is to assure seamless mobility and make applications comply with environment elements. This paper presents BROAD-RS (BROAD Recommendation System) architecture capable of performing context sensitive recommendation of learning objects based on an ontology for modeling the profile and context of the student in an e-learning environment implemented in a multi-agent system.