Analysis of the EXANI-II results in the State of Aguascalientes using data mining techniques

Abstract

Data mining techniques allow extracting the hidden knowledge in big data sets generated in any field, particularly in the educational field. With the help of this kind of techniques and specialized tools, it is being carried out an analysis of the EXANI-II data bases of the Aguascalientes State (México) corresponding to the 2013 year, whose main purpose is the identification of the factors that impact negatively the academic performance of senior high students, as well as the definition of strategies to reinforce the weak aspects identified in this performance. The models generated as fundamental part of this study will be validated in a subsequent study by using the data corresponding to the 2014 year, in such a way that the generated models have a high level of confidence at the moment of being used. A preliminary data analysis has suggested using mainly the techniques of classification (decision trees) and clustering (grouping by sector) in this study.