Implementing cluster analysis tool for the identification of students typologies

Abstract

The identification of students' typologies plays interesting role in adapting educational strategies and improving academic performances. In this work, we show how unsupervised learning techniques can be applied to educational data for the extraction of typologies and profiles of graduate students based on educational outcomes in combination with the time to degree. We also describe a web-based tool for clustering student's data, based on R programming and shiny, in order to make the clustering analysis, more accessible for university decision maker. The clustering tool presented in this article will enhance the understanding of different learning characteristics of graduate students and could be used to adapt teaching approaches and strategies according to the identified student profiles.