Implementation of machine learning techniques and creation of an artificial neural network for the prediction of the academic performance of students in university environments that use e-learning and streaming

This work describes the implementation of machine learning (ML) techniques: Random Forest, Xtreme Boosting Gradient, Support Vector Machine, K-Nearest-Neighbor and Logistic Regression as well as the creation of an artificial neural network (ANN), which were compared to determine the technique that can learn to predict with greater accuracy, the low academic performance of university students, to improve the mechanisms of e-learning and streaming that help them raise academic performance. The e-learning methodology was established for the first time in the late 1990s, however, since the Covid-19 pandemic, it has established itself as the best alternative to traditional education, placing it as a benchmark worldwide. One of the concerns in the university environment where this study was carried out is to be able to determine the impact that virtual teaching has had compared to face-to-face teaching, since there are factors (gender, number of children, sex, age, type of study) that could influence the academic performance of students. Using the classification metrics within the comparative process, it was determined that among the implemented ML techniques, the XGBoost reached 78.4% accuracy, but was surpassed by the artificial neural network (ANN) that learned to predict with 82.4%. of accuracy. Due to the above, the use of the artificial neural network is recommended for the prediction of the academic performance of university students since, in addition, with its massive predictions can be made due to its high processing capacity. Key Words: e-learning, covid19, streaming, academic performance, machine learning, artificial neural networks,