Model for discovering knowledge about academic and administrative aspects for students at driving schools in San Juan De Pasto

Authors

  • John Jairo Rivera Minayo Facultad de Ingeniería, Universidad de Nariño, Pasto, Colombia. Author
  • Javier Alejandro Jiménez Toledo Facultad de Ingeniería, Universidad de CESMAG, Pasto, Colombia. Author
  • Deixy Ximena Ramos Rivadeneira Facultad de Ciencias Administrativas y Contables, Universidad de CESMAG, Pasto, Colombia. Author
  • Jorge Albeiro Rivera Rosero Facultad de Ingeniería, Universidad de CESMAG, Pasto, Colombia. Author

DOI:

https://doi.org/10.56294/dm2025842

Keywords:

Educational data mining, Learning analytics, K-means, K-prototype, Driving schools, Knowledge discovery in databases (KDD)g

Abstract

 This paper proposes a comprehensive methodology for knowledge discovery in databases (KDD) applied 
to driving schools. The usefulness of clustering algorithms such as K-means and K-prototype to identify 
patterns in administrative and academic procedures was explored. During the study, three main stages were 
developed: process characterization, experimental design based on machine learning, and evaluation of 
the generated models. The results showed that K-prototype is particularly effective in handling mixed data, 
providing key recommendations to optimize both training processes and internal management. In addition, 
an application was designed to implement the model, highlighting the impact of educational data mining on 
dynamic analysis and informed decision making.

References

[1] Romero, C., & Ventura, S. (2020). "Educational Data Mining: A Review of the State of the Art." IEEE Transactions on Systems, Man, and Cybernetics: Systems, 50(6), 601–618.

[2] Baker, R. S., & Inventado, P. S. (2021). "Educational Data Mining and Learning Analytics: Second Edition." Springer.

[3] Peña-Ayala, A. (2021). "Learning Analytics: Fundaments, Applications, and Trends." Springer. Este libro proporciona una visión integral de la analítica de aprendizaje, abordando fundamentos, aplicaciones y tendencias actuales en el campo.

[4] Zhang, Y., & Rangwala, H. (2023). "Deep Learning Techniques for Educational Data Mining." ACM Computing Surveys, 55(1), 1–37.

[5] Kumar, V., & Chadha, A. (2022). "An Improved K-Prototypes Clustering Algorithm for Mixed - Numerical and Categorical Data." Expert Systems with Applications, 185, 115612. Este estudio presenta mejoras al algoritmo K-Prototypes, relevante para el análisis de datos mixtos en contextos educativos.

[6] Hidalgo Cajo, B. G. (2018). "Minería de datos en los Sistemas de gestión de Aprendizaje en la Educación Universitaria." Campus Virtuales, 7(2), 115–128.

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Published

2025-07-01

Issue

Section

Original

How to Cite

1.
Rivera Minayo JJ, Jiménez Toledo JA, Ramos Rivadeneira DX, Rivera Rosero JA. Model for discovering knowledge about academic and administrative aspects for students at driving schools in San Juan De Pasto. Data and Metadata [Internet]. 2025 Jul. 1 [cited 2025 Aug. 30];4:842. Available from: https://dm.ageditor.ar/index.php/dm/article/view/842