Competencies in Higher Education: A Feature Analysis with Self-Organizing Maps.
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2020
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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.
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Self-Organizing Maps, Generic competencies, Higher education