Title:
Predicting School Failure and Dropout by Using Data Mining Techniques [Download]Authors:
Marquez-Vera, Carlos and Morales, Cristóbal Romero and Soto, Sebastián Ventura
Index Terms:
Classification algorithms;Data mining;Prediction methods;Failure analysis;Writing;Decision trees;Behavioral science;Classification;Classification;dropout;educational data mining (EDM);prediction;school failure
Abstract:
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.
DOI:
How to cite:
Marquez-Vera, Carlos and Morales, Cristóbal Romero and Soto, Sebastián Ventura, "Predicting School Failure and Dropout by Using Data Mining Techniques" in IEEE Revista Iberoamericana de Tecnologias del Aprendizaje, pp. 7-14, Feb. 2013. doi: 10.1109/RITA.2013.2244695