Modelo neural por padrões proximais de aprendizagem para automação personalizada de conteúdos didáticos

Abstract

This study presents a model for the organization of educational content customized for environments of individual studies. For many students the availability of content in general form can not be efficient. It proposed a multilevel structure of concepts to provide the development of different combinations to show the same content. The work shows that it is possible to customize the content in order to encourage other students with the use of proximal learning standards. These patterns are obtained from the analysis of the action of students with positive results in the individual organization of the content. The formal representation establishes the definition of the student profile, multi-level content, the distribution plan of correction of concepts and teaching career. The structure of the trajectory of student teaching is formally established by the method of finite differences. The system uses artificial intelligence techniques to organize and personalize content reactively. Customization is provided by an artificial neural network that enables the classification of the student profile and assign that profile to a standard proximal learning. To mediate and adjust the contents of a reactive system was inserted into a set of rules from experts in teaching. The experiment showed the applicability and appropriateness of the proposed model. The results indicated the suitability of the approach by automating the organization\'s custom content so adaptive and reactive. The intelligent system to establish the structure of the custom content to be presented was considered efficient, giving the student a better use of the content, with higher and lower final average study time and content presented.