A Comparative Study between Clustering Methods in Educational Data Mining

This paper aims to describe the analysis of data from the Moodle's database of a beginner class in Distance Education of a Federal University using distinct educational data mining clustering methods. We carried out clustering using hierarchical and non-hierarchical methods in different groups of students, according to their interaction and performance characteristics. In the analysis, it was possible to perceive the groups obtained, a similarity between the results of each method used, confirming the acquired knowledge from the clustering and demonstrating that the choice of method in this study had little influence on the knowledge obtained from interactions and students performance on the course.