Title
Machine Learning Model through Ensemble Bagged Trees in Predictive Analysis of University Teaching Performance
Date Issued
01 January 2021
Access level
open access
Resource Type
journal article
Author(s)
Chamorro-Atalaya O.
Chávez-Herrera C.
Santos M.A.D.L.
Santos J.A.D.L.
Leva-Apaza A.
Peralta-Eugenio G.
Publisher(s)
Science and Information Organization
Abstract
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.
Start page
367
End page
373
Volume
12
Issue
12
Language
English
OCDE Knowledge area
Ciencias de la información
Ingeniería de sistemas y comunicaciones
Ciencias de la educación
Subjects
Scopus EID
2-s2.0-85122580274
Source
International Journal of Advanced Computer Science and Applications
ISSN of the container
2158107X
Sources of information:
Directorio de Producción Científica
Scopus