Modeling of an intelligent system for Education 4.0 using Bayesian networks and active methodologies

Education 4.0 was developed to modify the way students and teachers interact in the search for knowledge inside and outside the classroom. Artificial intelligence, associated with the well-known “skills of the future” requires a new perception of how the content from the sender of the source should be offered to the receiver. This research presents the modeling of a system, using Bayesian networks and active methodologies, to evaluate the school trajectory of students, until achieving a certain number of skills that were pre-selected. With a set of ten skills, a random quantity is selected, and these are tested and associated with a course, using active methodologies. A Bayesian network was designed to discover how performance in each discipline can contribute to building skills. Furthermore, when analyzing the notes and conditional probabilities, the Bayesian network allows the system to make suggestions for new tasks to develop and improve the knowledge in each discipline individually.