Classification of learning profile based on categories of student preferences

In an environment applied in engineering teaching, as in many knowledge areas, is very important to know and understand learner differences in a way to be able to adapt systempsilas actions to studentpsilas best learning conditions and aptitudes. Working thus makes it possible to identify learning profiles within a group of students, allowing the system to supply learners with contents and tools more suited for them. The goal of this work is to present the architecture of a system that realizes an evaluation of learning profiles based on categories of student preferences. The categories are defined from Felder-Silverman Learning Style Model. The architecture enables the teacher to specify the observable characteristics he considers most suitable within the teaching scope in question, whose characteristics are related with categories of student preferences. Through the categories create a relationship between what is observed and the learning objects used to build automatically the learning scenarios according to the student learning profile.