Modelo híbrido de sistema tutor inteligente utilizando conhecimento do especialista e mapas de Kohonen com treinamento automatizado

Abstract

The contemporary education has a lot of challenges and among them is the adaptation of using new technologies with classical education paradigm. It hasn\'t been different with distance education. In this context, this work proposes to develop a hybrid tutoring system model with decisions based on the teachers knowledge and help from Self Organizing Maps (SOM) or Kohonen\'s Maps neural networks. The proposed system has a initial teaching method that is set by specialist teacher up and while system is being running this pedagogic method is refined by the neural networks, which use patterns extracted from students that has used the system. The model proposes the utilization of a basic neural network structure with automated training which is capable of train several networks and define the one which represents results that is more coherent with the pattern\'s set, dismissing the intervention of a specialist on the evaluation of the network training performance. The system has adaptive and reactive features related to the apprentice, being able to offer to the students a personalized and dynamic learning. The system was developed in a web environment aiming avail the advantages of this technology. At this work, besides the proposed model developing it also were performed a data gathering with fresh students from integrated learning technical of Federal Institution of Goiás, Luziânia, Goiás, Brazil, to evaluate system\'s applicability. This thesis presents the fundamentals theorists of the virtual education environment, as also the artificial neural networks SOM, used on proposed model. Likewise, it shows the system developing process, the automated training build, in addition with the system tutor structure. The knowledge\'s transmission is inspired in the content\'s didactic transposition, with organization didactic units in levels that aim develop distinct skills. The SOM networks analysis indicate that the automated train was able to train several networks and identify a network with best topologic order. Moreover, this work presents a comparison between students performance when submitted to learn using the system with purely specialized orientation and hybrid orientation. The outcomes of this evaluation points out the viability of the proposed model, since the system has shown to be able to learn from students and adapt the teacher learning method. The apprentices that studied utilizing the system had amplified theirs grades on the learning system evaluations and the hybrid tutor was capable of take decisions which magnify the acceptation of the tutor learning indication.