Intelligent tutoring system using expert knowledge and Kohonen maps with automated training

This paper presents an intelligent tutoring system (ITS) model that is capable of driving the didactic transposition of contents. Initially, the tutoring system reactions bases its behavior on rules defined by an expert teacher; after this, a neural network that learns from the student’s behavior when they are studying adjusts these rules. This way, the neural network improves the teacher’s rules and, consequently, defines a learning strategy that is more adaptive and reactive to the student’s profile. Thus, it is possible to offer the student a personalized and individualized education form. The model is able to guide the student throughout the didactic transposition of contents, aiding the consolidation of desired competencies established on educational propositions. This work shows the development process of the ITS, including the expert guidance system and the hybrid system, which improves the expert rules from SOM neural network use. The obtained results indicate that the application of hybrid technology in ITSs is feasible because, for defining the teaching strategies, it incorporates the teacher’s knowledge and by neural network use, it assimilates the students’ learning process behavior. The results show that proposed model has great agreement between the actions of the “ITS” and the students’ actions. The model showed satisfactory performance when compared to other systems proposed in the literature that use connectionist approach in its conception.