RPOIA: A Method of Selecting Learning Objects Using Petri Nets - de Carvalho, Joethe Moraes; Lima, Josias Gomes; de Magalhaes Netto, Jose Francisco; Caldas, Ruiter Braga (Proceedings - Frontiers in Education Conference, 2020)
This Research Full Paper presents a method to select Learning Objects based on students' cognitive profiles, aiming to facilitate the explained content understanding and programming skills improve. STEM approach has favored the programming skills development in Computer Science courses. Due to initial concepts complexity, many students find it difficult to understand this discipline, causing demotivation or course abandonment. We propose a method using Petri Nets formalism to select a Learning Object that addresses the subject being studied. Petri Nets are formal description techniques used to specify competing systems through graphical and mathematical modeling. Intelligent Agents are computer programs created to automate and perform a certain task or direct interaction with student and propose solutions considered appropriate, based on based on knowledge obtained during the interactions. This research uses the Petri Net to create an Intelligent Agent model, which chooses the Learning Object from answers obtained by applying VAK, an educational questionnaire and results obtained shown to be promising and method was well evaluated by students.