PROBLEM-BASED LEARNING AS A STRATEGY FOR TEACHING THE FUNDAMENTALS OF KNOWLEDGE REPRESENTATION SYSTEMS: THE EXPERIENCE WITH CELLULAR AUTOMATA

Abstract

Of an empirical, inter and transdisciplinary character operated by the descriptive qualitative approach, this article aims to describe and analyze a problem-based learning process as a teaching strategy of the Knowledge Representation System discipline. Problem-based learning is an active methodology that has been widely used in the discipline and has guided the entire teaching and learning process in view of the subtheme Cellular Automata and the challenge of understanding the modeling and representation of knowledge implemented by the Turing Machine. Problem-based learning was fundamental in the process, either because of its inter- and transdisciplinary character, or because of its autonomy, which gives students the possibility of managing their learning.