Um sistema de apoio à tomada de decisão no acompanhamento do aprendizado em educação a distância.

Abstract

In Distance Education - DE, the learning accompaniment is aggravated by the lack of body-to-body contact between teachers and students and by the lack of a specific pedagogical practice of accompaniment for such a teaching modality. This way, in DE, it is necessary to elaborate new models and strategies which represent the learning status of the remote student. This document proposes a learning accompaniment strategy suitable for DE based on accompaniment theories of presencial teaching added with data analysis tactics, where the accompaniment factors can be related together in order to verify the learning process in a more elaborated form. It presents the Midas-Poeta decision support system, using Data Mining algorithms, for the Portfolio-Tutor environment. The goal of such a system is to provide standards which characterize student’s behavior.