Diagnosis of the Learning Process of Artificial Intelligence Students through a Dynamic Bayesian Model

Abstract

One of the main objectives of Education for this century is to instill cognitive skills that ensure information research and comprehension through a critical reading. Not only are these skills desirable in any Engineering career, but they have also become critical in disciplines such as ‘Artificial Intelligence’, where innovations are given on a daily basis. In this sense, Bayesian Networks are a type of Intelligent System capable of identifying students' learning style. Nonetheless, they fail to represent the manner in which said knowledge evolves. Therefore, this paper proposes to apply a Dynamic Model in order to diagnose students' learning process and provide a better understanding of their behavior