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-experimental design were undertaken. The research methodology followed a hypothetical-deductive approach. The number of participants was 157 and a registration form was used to collect data on the indicators that made up the academic performance variable. The results reveal that the switch to a virtual teaching–learning modality significantly influenced the academic performance of the students. Student’s t-test found a significance equal to 0.000. Passing grades were achieved by 98.57% of students under the virtual teaching–learning modality, compared to 68.4% under classroom learning.
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.
<span>The objective of the research is to analyze the satisfaction of the online learning of the applied electricity subject, when implementing technological tools for virtual teaching. The development of the research determines a high level of student satisfaction, finding the perception of reliability with 93.05%, that of security with 93.2%, that of answer’s capacity with 90.73% and empathy with 82.87%. Satisfaction with the technological tools of virtual teaching is related to the adequate and accessible use of simulation software during online learning, which allowed compliance with the syllable. In addition to the security and confidence when the teacher is willing to help him in the use of the simulation software, responding to it appropriately and quickly. Satisfaction of online learning of the applied electricity subject using virtual teaching tools is related to the teacher's sample of concern towards students regarding their academic needs and their expressed interests.</span>
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.
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