Prediction of university dropouts through random forest-based models
Fred Torres‐Cruz, Elqui Yeye Pari-Condori, Ernesto Nayer Tumi-Figueroa, Leonel Coyla-Idme, Jose Tito-Lipa, L.M. González, Alfredo Tumi-Figueroa
Abstract
This study presents a solution for predicting university dropout rates, leveraging advanced digital technologies and the Random Forest algorithm. The model was developed using key academic variables, such as year of enrollment, program of study, semester attended, and academic performance, represented by the grade point average (GPA). A dropout threshold was established for students whose GPA fell below 11. The dataset was partitioned into 70% for training and 30% for testing, yielding an overal
Topics & Concepts
Random forestStatisticsMathematicsForestryComputer scienceGeographyArtificial intelligenceArtificial Intelligence in HealthcareCOVID-19 diagnosis using AIOnline Learning and Analytics