Sequenciamento e recomendação de ações pedagógicas baseados na Taxonomia de Bloom Revisada e no RASI utilizando PSO multiobjetivo

Abstract

According to student needs, learning can be supported and enhanced through structured and personalized instruction. This work presents an approach to personalized pedagogical recommendations based on the student’s cognitive profile, given by the Revised Approaches to Studying Inventory (RASI). The recommended pedagogical actions follow the hierarchy of Revised Bloom’s Taxonomy. We model the sequencing of pedagogical actions as a multi-objective optimization problem. This problem solution was implemented using a Particle Swarm Optimization (PSO) algorithm. The optimized objectives in this problem are the similarity between the student’s profile and the sequence of actions, and the number of actions appropriate to the student’s profile. Experiments conducted with students in higher education institutions suggest that the proposed approach using PSO presents solutions that are better accepted by students than the randomized pedagogical recommendation.