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Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.
Supervised Learning through Classification Learner Techniques for the Predictive System of Personal and Social Attitudes of Engineering Students
Digital Object Identifier (DOI) : 10.14569/IJACSA.2021.0121289
Article Published in International Journal of Advanced Computer Science and Applications(IJACSA), Volume 12 Issue 12, 2021.
Abstract: In this competitive scenario of the educational system, higher education institutions use intelligent learning tools and techniques to predict the factors of student academic performance. Given this, the article aims to determine the supervised learning model for the predictive system of personal and social attitudes of university students of professional engineering careers. For this, the Machine Learning Classification Learner technique is used by means of the Matlab R2021a software. The results reflect a predictive system capable of classifying the four satisfaction classes (1: dissatisfied, 2: not very satisfied, 3: satisfied and 4: very satisfied) with an accuracy of 91.96%, a precision of 79.09%, a Sensitivity of 75.66% and a Specificity of 92.09%, regarding the students' perception of their personal and social attitudes. As a result, the higher institution will be able to take measures to monitor and correct the strengths and weaknesses of each variable related to satisfaction with the quality of the educational service.
Keywords: Supervised learning; classification learner; predictive system; personal and social attitudes; engineering students
Omar Chamorro-Atalaya, Soledad Olivares-Zegarra, Alejandro Paredes-Soria, Oscar Samanamud-Loyola, Marco Anton-De los Santos, Juan Anton-De los Santos, Maritte Fierro-Bravo and Victor Villanueva-Acosta, “Supervised Learning through Classification Learner Techniques for the Predictive System of Personal and Social Attitudes of Engineering Students” International Journal of Advanced Computer Science and Applications(IJACSA), 12(12), 2021. http://dx.doi.org/10.14569/IJACSA.2021.0121289
@article{Chamorro-Atalaya2021,
title = {Supervised Learning through Classification Learner Techniques for the Predictive System of Personal and Social Attitudes of Engineering Students},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2021.0121289},
url = {http://dx.doi.org/10.14569/IJACSA.2021.0121289},
year = {2021},
publisher = {The Science and Information Organization},
volume = {12},
number = {12},
author = {Omar Chamorro-Atalaya and Soledad Olivares-Zegarra and Alejandro Paredes-Soria and Oscar Samanamud-Loyola and Marco Anton-De los Santos and Juan Anton-De los Santos and Maritte Fierro-Bravo and Victor Villanueva-Acosta}
}
