This article aims to analyze the satisfaction of university students from the per-spective of teaching performance, in face-to-face (2019-II) and virtual (2020-I) teaching-learning, due to the health emergency, declared in Peru, by COVID-19. These results will allow the Public University to implement continuous improve-ment plans in the teaching-learning development of the virtual environment. When performing the comparative analysis, it was determined that the careers that present the greatest satisfaction in 2020 - I, are business administration with 82.97% and systems engineering with 78.07%. Then it was identified that the in-dicators that present a greater negative variation are “The quality of the develop-ment of classes and activities“ with 5.88%, and “Treatment of students during class“, with 2.49%. With these results it can be indicated that the satisfaction of the stude...

This article aims to analyze the satisfaction of university students from the per-spective of teaching performance, in face-to-face (2019-II) and virtual (2020-I) teaching-learning, due to the health emergency, declared in Peru, by COVID-19. These results will allow the Public University to implement continuous improve-ment plans in the teaching-learning development of the virtual environment. When performing the comparative analysis, it was determined that the careers that present the greatest satisfaction in 2020 - I, are business administration with 82.97% and systems engineering with 78.07%. Then it was identified that the in-dicators that present a greater negative variation are “The quality of the develop-ment of classes and activities“ with 5.88%, and “Treatment of students during class“, with 2.49%. With these results it can be indicated that the satisfaction of the stude...

—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 ...

Faced with Covid-19, and the need to adapt to environments that guarantee continuity of educational service in the context of social distancing, many universities did not initially plan the mechanisms for adapting to the virtual modality adequately. Therefore, this period of transition to e-learning was characterised by a decrease in academic performance . This article reports on a study that focused on determining whether the transition from a classroom to a virtual teaching–learning model had an effect or influence on the academic performance of university students in mechanical and electrical engineering at a public university in Peru during the period 2018 to 2021. The purpose of the study was to ensure the quality of the education system in the face of the implementation of a hybrid mode of teaching. Methodologically, a descriptive type of investigation and longitudinal non-experi...

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 h...