期刊名称：Anais dos Workshops do Congresso Brasileiro de Informática na Educação
印刷版ISSN：2316-8889
出版年度：2017
卷号：6
期号：1
页码：654
DOI：10.5753/cbie.wcbie.2017.654
语种：Portuguese
出版社：Anais dos Workshops do Congresso Brasileiro de Informática na Educação
摘要：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.
