OntAES: Uma Ontologia para Sistemas Adaptativos Educacionais Baseada em Objetos de Aprendizagem e Estilos de Aprendizagem

Abstract

Several approaches to custom recommendation of content in adaptive systems for education have emerged. Many have conglomerated the importance of taking into account student learning styles so that better results can be achieved in the learning process. Recent studies confirm this trend.

This reserch aims to present a proposal for the recommendation of learning objects based on the IEEE LOM standard and the Felder-Silverman Learning Styles model. In addition, an ontology has been developed to support this approach.

As a result, the proposed ontology generates a vector representation of learning objects considering the learning styles satisfied by them. The ontology, called OntAES, follows the entire teaching process, from the insertion of OAs and students into the system, to classification and ranking in a personalized and automatic way.

In addition, it presents a comparative approach between how to calculate the OA ranking for each student. The ontology was validated through experiments, and promising results were obtained, demonstrating the potential contribution of this proposal to adaptive systems for education.