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This study uses advanced data science techniques to explore the variables that influence university dropout rates. Through predictive models and the integration of demographic, socioeconomic, and academic data, key factors are identified and risk estimates are provided, with the goal of guiding interventions to improve student retention.

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Analysis of Predictive Factors in University Dropout Rates Using Data Science Techniques

  • 会议论文

  • 发表日期：

    2025年

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  • Alejandro Mauricio Maqueo-Huerta,
  • Emilio Yoltic Martínez-Gutiérrez,
  • Rogelio Zaid Sariñana-Hernández,
  • Favio Mariano Dileva-Charles,
  • Neil Hernandez-Gress

摘要

This study uses advanced data science techniques to explore the variables that influence university dropout rates. Through predictive models and the integration of demographic, socioeconomic, and academic data, key factors are identified and risk estimates are provided, with the goal of guiding interventions to improve student retention.