Prediction of Labor Insertion in Graduates Based on the Pre-Professional Internship Process Through a Supervised Machine Learning Model

Arana M.; Camborda M.; Calderon S.; Castro F.; Arana M.; Huaricapcha B. · Proceedings - 10th International Symposium on Accreditation of Engineering and Computing Education, ICACIT 2024 (2024) · DOI: 10.1109/icacit62963.2024.10788618

Resumen

This research sought to implement a predictive model using machine learning algorithms to determine if a student graduated from the Faculty of Systems Engineering of the National University of Central Peru (hereinafter FIS-UNCP) is able to enter the world of work taking into account employers and competences during the pre-professional internship process. The research was carried out following the scientific-systemic method, of a quantitative approach, of applied type and explanatory-predictive level, where the design was nonexperimental longitudinal trend, the chosen population took into account the 355 students of the FIS-UNCP which managed to complete the pre professional internship process from 2023-I to the present. The results showed that the predictive model had an assertiveness of 92.96% allowing to efficiently predict the employment of graduates taking into account the process of pre professional practices, with a T-value = 22.909 and P-value = 0.000, evidencing that the pre professional internship process significantly influences the employment insertion of graduates of the FIS UNCP through a predictive model supervised in machine learning.

Instituciones: Universidad Continental; Universidad Peruana Los Andes; Universidad Nacional del Centro del Perú