Machine Learning: Vocational Guidance and the Profile of the Student Entering a Study Program

Abstract

Admission to a higher education institution is oriented to principles and methods established by them, for admission or not, the applicant tries to demonstrate his or her competencies to choose an ideal professional career, the university accreditation model in its standard 18 reference on admission to the study program and that must be in accordance with the admission profile; Machine learning through the decision tree technique becomes indispensable for the solution of human behavior problems since through binary regions it identifies groupings with homogeneous data, the objective of this research was to develop a decision tree based on a neural network that allows classifying the vocation of the applicant to a certain study program based on the profile of the entrant; To ensure that the applicant knows his/her competencies, the knowledge, aptitudes and attitudes raised have been considered, the neural network considered 3 incoming axon's and one outgoing axon; Finally, the decision tree contains 4 levels of information, the result of the tree model gives a value of 0.939, a value that guarantees the application of the global model for the prediction of the entry or not of applicants to certain careers, also this model is supported by the sensitivity results with values of 0.818 and 0.983 for the classes "yes" or "no" enters respectively,  A cross-sectional analytical study has been carried out.