Machine learning model through ensemble bagged trees in predictive analysis of university teaching performance

• Autores: Leva Apaza, Antenor, Chamorro-Atalaya, Omar, Anton-De los Santos, Marco, Anton-De los Santos, Juan, Chávez-Herrera, Carlos, Torres-Quiroz, Almintor, Tasayco-Jala, Abel, Peralta-Eugenio8, Gutember
• Formato: artículo
• Fecha de Publicación: 2022
• Institución: Universidad Tecnológica del Perú
• Repositorio: UTP-Institucional
• Lenguaje: inglés
• OAI Identifier: oai:repositorio.utp.edu.pe:20.500.12867/5627
• Enlace del recurso: https://hdl.handle.net/20.500.12867/5627; http://doi.org/10.14569/IJACSA.2021.0121249
• Nivel de acceso: acceso abierto
• Materia: Machine learning; Teacher performance; Predictive analytics; https://purl.org/pe-repo/ocde/ford#5.03.01; https://purl.org/pe-repo/ocde/ford#2.02.03

The objective of this study is to analyze and discuss the metrics of the Machine Learning model through the Ensemble Bagged Trees algorithm, which will be applied to data on satisfaction with teaching performance in the virtual environment. Initially the classification analysis through the Matlab R2021a software, identified an Accuracy of 81.3%, for the Ensemble Bagged Trees algorithm. When performing the validation of the collected data, and proceeding with the obtaining of the predictive model, for the 4 classes (satisfaction levels), total precision values of 82.21%, Sensitivity of 73.40%, Specificity of 91.02% and of 90.63% Accuracy. In turn, the highest level of the area under the curve (AUC) by means of the Receiver operating characteristic (ROC) is 0.93, thus considering a sensitivity of the predictive model of 93%. The validation of these results will allow the directors of the higher institution to have a database, to be used in the process of improving the quality of the educational service in relation to teaching performance.