Evoluindo inteligências múltiplas pelo método da espiral de aprendizagem utilizando Particle Swarm Opimization

Abstract

Learning is a paradigm that comes with being human. Increasingly, technologies are being included in this scenario providing alternative ways of aggregating the inclusion and propose ways to help in the teaching process. This inclusion is well seen in long distance learning courses or semi presential classes, in which computers are used. When each student personal characteristics are considered and used to assist them in the learning process, an effective way to present a subject of study that is adaptable to each student in particular in unraveled. The main objective of this work is to present a computational model using an optimization technique to offer the student in a course aimed at computer, a teaching strategy that best suits his profile. For that reason, it is important to know which pedagogical technique to use, in this case, were used: the Spiral Learning by David Kolb and Gardner\'s Multiple Intelligences. The proposed computational model uses Particle optimization or swarm optimization which has the role to suggest the student the Learning object through the evolution on Kolb\'s Spiral Learning. It also improves the percentage of their Multiple Intelligences. Therefore, the purpose of this work is to contribute to: development of programs personalized to each student and the use teaching techniques that lead to the evolution and promote the choice of appropriate digital teaching materials.As a result it is shown the evolution of students with profiles prevalent in certain learning styles and multiple intelligences.