Adaptive learning in the educational e-LORS system: an approach based on preference categories

In the field of electronic education, the recommendation of contents with higher levels of relevance may potentially attract the students’ attention. In this context, this work considers students’ learning styles, delineated with structured questionnaires, as a means of selecting the best content as for the learning-teaching process. The goal is to present a complete systematisation – the e-LORS system, which is able to recommend electronic educational content based on the relationship between detected learning styles and stored learning objects. Our contributions include the e-LORS system – its multiple-criteria architecture and study case, the methodology based on the Felder-Silverman learning style model and on the IEEE learning object metadata (LOM), and the reporting of experiments conducted in an actual educational context. 342 L.A.M. Zaina et al.