Analysis of Student Dropout in Industrial Engineering Students Using Computational Intelligence Techniques

Industrial Engineering attracts diverse students because it offers versatile career opportunities; however, student attrition remains a significant concern. This study employs computational intelligence techniques to analyze a data set from the U niversidad Distrital Francisco Jose de Caldas. Through data refinement processes, including statistical analysis, clustering methods and predictive modeling, our goal is to ensure data integrity and unveil the dynamics of student dropout in Industrial Engineering. We also present the effectiveness of computational intelligence in understanding dropout behavior. The study provides valuable information for education professionals and policymakers.