Methodological Framework Based on Longitudinal Machine Learning, Explainability and Algorithmic Fairness to Predict the Average University Graduation Rate Using Multidimensional Variables

11CP26-34

Authors

Keywords:

academic performance, longitudinal analysis, explainability, algorithmic fairness, higher level

Author Biography

Amanda Castro Ochoa, Universidad Autónoma de Ciudad Juárez

PhD in Advanced Engineering Sciences, Departamento de Ingeniería Industrial y Manufactura, Instituto de Ingeniería y Tecnología, Universidad Autónoma de Ciudad Juárez, México

Portada de Memorias Científicas y Tecnológicas

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Published

2026-06-17

How to Cite

[1]
A. Castro Ochoa, “Methodological Framework Based on Longitudinal Machine Learning, Explainability and Algorithmic Fairness to Predict the Average University Graduation Rate Using Multidimensional Variables: 11CP26-34”, Mem. Científ. y Tecnol., vol. 5, no. 4, pp. 69–70, Jun. 2026.

Issue

Vol. 5 No. 4 (2026): 11th IIT Postgraduate Colloquium

Section

11.º Coloquio de Posgrados del IIT