e-LORS: Uma Abordagem para Recomendação de Objetos de Aprendizagem

The use of recommender systems in order to improve human-computer interaction has increased significantly in applications as e-commerce and e-learning. Such systems aim at suggesting information and services that can be attractive to the users based on the automatic use of data that describe their preferences. In the educational area, the recommendation of relevant and interesting contents can be effective for catching the students' attention and driving higher motivation during the learning-teaching process. The ability to provide contents of their preferences considering the students' learning style may increase substantially the success of such effort. In this context, the goal of this work is to present the e-LORS system, an approach for recommendation of electronic content based on the relationship between learning styles and learning objects. In our methodology, the learning profiles are described according to discrete dimensions in order to support different perspectives concerning the students' preferences. Our recommendation methodology, in turn, uses these dimensions to filter the learning objects – described by the IEEE LOM standard – that are more suitable to the students.