Mexican International Conference on Artificial Intelligence

Advances in Artificial Intelligence and Its Applications pp 208-219 | Cite as

Applying Data Mining Techniques to Identify Success Factors in Students Enrolled in Distance Learning: A Case Study

  • José Gerardo Moreno Salinas
  • Christopher R. Stephens
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9414)

Abstract

Distance learning is now a key component in higher level education. Given the high dropout rates and the important investments in distance learning it is of utmost concern to determine the most critical data in the success and failure of students. In this article we data mine enrollment profiles, educational background and students´ data from the Open University System and Distance Learning of the National Autonomous University of Mexico to determine the key factors that drive success and failure, creating a relevant predictive model using a Naive Bayes classifier. We have found that the number of subjects approved and their average qualification in the first semester are part of the most interesting predictors of student success.

Keywords

Distance learning Keys to success Data mining Naive Bayes classifier 

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • José Gerardo Moreno Salinas
    • 1
  • Christopher R. Stephens
    • 2
  1. 1.Coordinación de Universidad Abierta y Educación a Distancia (CUAED) – UNAMCoyoacánMexico
  2. 2.Centro de Ciencias de la Complejidad (C3) e Instituto de Ciencias Nucleares – UNAMMexico CityMexico

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