Predicción del Fracaso Escolar Mediante Técnicas de Minería de Datos

• Autores: C. Márquez Vera, C. Romero, Sebastián Ventura Soto
• Localización: Revista Iberoamericana de Tecnologías del Aprendizaje: IEEE-RITA, ISSN 1932-8540, Vol. 7, Nº. 3, 2012, págs. 109-117
• Idioma: español
• Títulos paralelos:
  • Predicting of School Failure Using Data Mining Techniques.
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• Dialnet Métricas: 10 Citas
• Resumen

  • This paper proposes to apply data mining techniques to predict school failure and drop out. 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 drop out by: firstly, 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 best resulting models are shown.