Academic Performance Evaluation Using Data Mining in Times of Pandemic

Abstract

This work focuses on studying the relationship that existed between the use of the learning management system (LMS) and the academic performance of the students of the Jorge Basadre Grohmann National University of Tacna-Perú. For this, we use the data provided by the LMS (access virtual classroom) and the university's academic management system (grades). For that, we perform various classification machine learning algorithms to predict academic performance with two classes SATISFACTORY or POOR where Gradient Boosted Trees algorithm had the best accuracy 91.79%. However, with three classes, SATISFACTORY, REGULAR AND POOR, Random Forest algorithm had the best accuracy of 89.26%.