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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.

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

  • 会议论文

  • 发表日期：

    2026年

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  • Diego Ricardo Salazar-Armijos,
  • Héctor Mauricio Revelo-Herrera,
  • Holger Alfredo Zapata-Mayorga,
  • Paúl Díaz-Zuñiga,
  • Aída Noemy Bedón-Bedón,
  • Nelson Fernando Vinueza-Escobar

摘要

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.