Statistical and ML Analysis to Determine the Factors That Influence Student Dropout Rates in Information Technology Programs

Abstract:

This research, conducted within the framework of the project “Dropout in Higher Education—Early Warning Model with Emerging Technologies at the University of the Armed Forces ESPE”, analyzed the factors influencing the dropout of Information Technology students at the Santo Domingo campus between 2017 and 2023. Socioeconomic and academic variables were considered, based on enrollment data in accordance with the regulations of higher education in Ecuador. Logistic regression and decision tree algorithms were applied due to their classification capabilities and statistical relevance. Additionally, an ANOVA-based comparison was performed. The study concluded that academic performance is the main factor associated with student dropout.

Año de publicación:

2026

Keywords:

• Decision Trees
• logistic regression
• Machine Learning
• Student dropout
• Supervised learning

Fuente:

scopus