Predicting School Failure Using Data Mining.
Carlos Márquez‐Vera, Cristóbal Romero, Sebastián Ventura · 2011
This paper proposes to apply data mining techniques to predict school failure. We have used real data about 670 middle-school students from Zacatecas, México. Several experiments have been carried out in an attempt to improve accuracy in the prediction of final student performance and, specifically, of which students might fail. In the first experiment the best 15 attributes has been selected. Then two different approaches have been applied in order to resolve the problem of classifying unbalanced data by rebalancing data and using cost sensitive classification. The outcomes of each one of these approaches using the 10 classification algorithms and 10 foldcross validation are shown and compared in order to select the best approach to our problem.