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Model to pbkp_redict academic performance based on neural networks and learning analytics

Abstract:

An element of great importance for university educational institutions, educators and students is the academic performance of them in the transition of their professional training. The mining of educational data develops models and methods to explore the data collected from the educational learning environments through learning analytics in order to detect patterns that allow pbkp_redicting variables of interest. The present research describes a pbkp_redictive model of academic performance using neural network techniques on a set of real data of 300 students of the Systems career of the Central University of Ecuador. This registration was provided by the virtual learning environment https://uvirtual.uce.edu.ec/developed in Moodle and used in said University.