Competencies in Higher Education: A Feature Analysis with Self-Organizing Maps.

Abstract

Students are supposed to accomplish with a set of generic competencies when they finish their studies. One of the major challenges in Universities is to detect shortcomings in students in order to strengthen them, so they could accomplish with the competencies required for a professional career. In this paper, unsupervised machine learning techniques as Self-Organizing Maps are used to analyze features of students from the bachelor’s degree in Psychology. The approach is clusterization students’ profiles in their first course of college to identify potential improvement areas. The dataset contains 16 features from 54 individuals. Results show that clusters differentiate mostly on the organizational and social competencies on one side, and neuroticism and agreeableness on the other.