Neural Networks to Predict Schooling Failure/Success

  • Authors:
  • María Angélica Pinninghoff Junemann;Pedro Antonio Salcedo Lagos;Ricardo Contreras Arriagada

  • Affiliations:
  • Informatics Engineering and Computer Science Department, Research and Educational Informatics Department, Universidad de Concepción, Chile;Informatics Engineering and Computer Science Department, Research and Educational Informatics Department, Universidad de Concepción, Chile;Informatics Engineering and Computer Science Department, Research and Educational Informatics Department, Universidad de Concepción, Chile

  • Venue:
  • IWINAC '07 Proceedings of the 2nd international work-conference on Nature Inspired Problem-Solving Methods in Knowledge Engineering: Interplay Between Natural and Artificial Computation, Part II
  • Year:
  • 2007

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Abstract

This paper depicts an already developed experience in search for a predictable mechanism with respect to the future performance of a student considering the numerous factors that influence in its failure/success. The use of different neural networks configurations in conjunction with a large data volume on top of detailed attributes consideration for each student makes for an adequate base for the results obtained to be analyzed. The idea behind this paper is to arrange a mechanism that allows us to estimate before hand taking into consideration data from the student in reference to family, social and wealth surroundings for the student future performance identifying those factors that favors the tendency to failure or success.