Features selection through FS-testors in case-based systems of teaching-learning

Abstract

The development of intelligents teaching-learning systems depends, on one hand, of the pedagogical paradigms and, on the other hand, of the available technologies to implement these paradigms in computers. The field of the Intelligent Teaching-Learning Systems is characterized by the application of Artificial Intelligence techniques, to the development of the teaching-learning process assisted by computers, where the term "intelligent" is associated to the student's aptitude to dynamically acclimatize to the teaching process by carrying out an individual learning. The case-based reasoning is an Artificial Intelligence technique that performs their reasoning process based on previously solved cases, stored in case-bases. In this article we propose a new case-based approach with foundations on fuzzy pattern recognition to help elaborate intelligents teaching-learning systems, using the KS-testor theory, based on a combination of typical testor theory with the fuzzy sets, assures the efficient access and retrieval of cases. © Springer-Verlag Berlin Heidelberg 2007.