Modelagem e Visualização Científica de Dados Educacionais: Estudo de Caso sobre o Desempenho em Componentes Curriculares

Resumo

The main objective of this paper is to investigate how scientific modeling and visualization techniques can enhance the interpretation of educational data. For data modeling, k-means algorithm and correspondence analysis were used for clustering and dimensionality reduction, respectively. For data visualization, boxplot, violin chart and perceptual map techniques were analyzed. The analyses were performed using academic data from two courses of Instituto Federal do Rio Grande do Norte in the period from 2008 to 2016. One of the obtained results shows that students grades follow multimodal distributions, indicating that the use of classic boxplots is not adequate.