Abstract:

This article analyzes the impact of university quality accreditations on the results of the national assessment test of higher education in Colombia for Industrial Engineering programs. The research objective is to evaluate if quality accreditations generate a positive impact on the academic results of higher education institutions. The methodology used in the paper lies in the articulation of supervised and unsupervised machine learning techniques for predicting the future accreditation state of a university. The results shows that does exists significant differences between the results of accredited universities vs No accredited universities. The Random Forest classifier predict correctly the belonging of the two classes with 85% of accuracy.