Uma Análise de Algoritmos de Clusterização para Descoberta de Perfis de Engajamento

Abstract

Distance education (EAD) has been taking great proportions in access to education, due to the flexibility of time and geographic location. However, despite the adoption by educational institutions, the number of graduates in this modality has low rates. Due to student engagement and interaction it is directly related to the factors of academic performance and motivation of students. This article aims to describe different grouping methods that were used in a database to identify groups with different engagement profiles. The results point to three profiles that, when analyzing them, methodologies can be studied to avoid dropouts in the course.