(IJACSA) International Journal of Advanced Computer Science and Applications, Vol. 12, No. 12, 2021 Machine Learning Model through Ensemble Bagged Trees in Predictive Analysis of University Teaching Performance Omar Chamorro-Atalaya 1 Facultad de Ingeniería y Gestión Universidad Nacional Tecnológica de Lima Sur Lima- Perú Carlos Chávez-Herrera 2 Facultad de Ingeniería de Sistemas e Informática Universidad Nacional Mayor de San Marcos Lima-Perú Marco Anton-De los Santos 3 Juan Anton-De los Santos 4 Facultad de Ciencias Económicas Universidad Nacional Federico Villarreal, Lima-Perú Almintor Torres-Quiroz 5 Facultad de Ciencias Económicas Universidad Nacional del Callao Lima-Perú Antenor Leva-Apaza 6 Facultad de Ciencias Universidad Tecnológica del Perú Lima-Perú Abel Tasayco-Jala 7 Gutember Peralta-Eugenio 8 Facultad de Ciencias Empresariales and Facultad de Ciencias de la Salud Universidad César Vallejo Lima-Perú 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. Keywords—Machine learning; ensemble; bagged trees; predictive analysis; teaching performance I. INTRODUCTION The information and communication technology (ICT) sector is currently a leader in the analysis of data from different media [1], [2], such as virtual platforms, survey administration software, among other technological tools [3], [4], which capture or acquire information to be processed and analyzed in descriptive statistical research or in research on predictive models applicable to various areas of knowledge [5]. The advantages that the introduction of ICT has generated in the education sector is based on the importance of technology to develop research that previously could not be carried out, [6], [7] as is the case of the identification of predictive models for the analysis or monitoring of university teaching performance, student performance, among other relevant factors for the education sector [8]-[10]. Worldwide, the education sector has undergone changes and transformations, due to the virtualization of the teaching- learning mode, [11], [12], [13], as a consequence of this scenario, universities face new challenges, to safeguard the quality of education that goes hand in hand with the advancement of technology [14]-[16]. Given this, in the education sector, an increasing amount of data has been generated with greater relevance, product of the iterations of the different actors of the educational process, these being the teacher, the students and the institution, through the application of tools technological, such as survey software, which generate a database [17], [18]. As indicated, the data that are stored, are used in order to improve the efficiency of the educational process through predictive models, among the factors to optimize are academic performance, student dropout, teaching performance, graduate follow-up [19]. There are various technologies used to obtain predictive models, which use data from virtual platforms and survey administration software, applied to students by universities [20]. Within these technologies is the branch of Artificial Intelligence that within its fields houses Machine Learning [21]-[23]. As indicated in [24], Machine Learning is a set of algorithms capable of learning to perform certain tasks from the generalization of examples. Machine Learning has been successfully applied to a variety of areas of human endeavor, and has recently been applied to the educational sector, whose purpose is oriented towards the design of algorithms, methods and models, which will allow the exploration of data from teaching-learning environments [25], [26]. Among the multiple algorithms of Machine Learning, there is Ensemble Bagged Trees, which is an algorithm that is used in joint learning [27]. This can combine training and base 367 | Page www.ijacsa.thesai.org