Predicting School Failure and Dropout by Using Data Mining Techniques

• Carlos Marquez-Vera ^[1] ; Cristóbal Romero Morales ^[2] ; Sebastián Ventura Soto ^[2]
  1. [1] Universidad Autónoma de Zacatecas

     Universidad Autónoma de Zacatecas

     México

     
  2. [2] Universidad de Córdoba

     Universidad de Córdoba

     Cordoba, España

     
• Localización: Revista Iberoamericana de Tecnologías del Aprendizaje: IEEE-RITA, ISSN 1932-8540, Vol. 8, Nº. 1, 2013, págs. 7-14
• Idioma: inglés
• DOI: 10.1109/rita.2013.2244695
• Texto completo no disponible (Saber más ...)
• Dialnet Métricas: 1 Cita
• Resumen

  • This paper proposes to apply data mining techniques to predict school failure and dropout. We use real data on 670 middle-school students from Zacatecas, México, and employ white-box classification methods, such as induction rules and decision trees. Experiments attempt to improve their accuracy for predicting which students might fail or dropout by first, using all the available attributes; next, selecting the best attributes; and finally, rebalancing data and using cost sensitive classification. The outcomes have been compared and the models with the best results are shown.

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