Sistema de recomendação de pares em ambientes educacionais com base nas múltiplas dimensões do perfil estudantil

Abstract

The current educational dynamics demand methodologies that encourage student interaction and protagonism in the learning process. In this context, strategies such as Peer Instruction have gained prominence for promoting the development of cognitive and socioemotional skills. However, manually forming student pairs poses a challenge for educators, especially when aiming to consider the students’ multidimensional profiles. Existing approaches in the literature are generally limited to using these profile dimensions in isolation, which may compromise the quality of collaboration. This work proposes the development of a peer recommendation system based on the complementarity of multiple student profile dimensions, using Genetic Algorithms as an optimization technique to form complementary pairs. The solution aims to support teachers in creating collaborative interactions among students, contributing to the effectiveness of collaborative learning, particularly Peer Instruction, in the classroom.