Probabilistic Classifiers and Statistical Dependency: The Case for Grade Prediction

  • Bakhtiyor Bahritidinov
  • Eduardo SánchezEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10338)


The research presented here aims at predicting grades by means of a set of relevant student’s variables. To solve this problem, a probabilistic approach was applied in which we assume that the probability of obtaining a certain grade is conditioned on the personal attributes of each student. A Bayesian classifier was the natural choice to include the student’s attributes in the estimation of the likelihood. However, a striking result was observed when the accuracy of the bayesian prediction was lower than the one provided by a baseline predictor based on student’s clustering. A follow-up analysis explains the reason behind this result and provides a guideline for similar classification problems.


Probabilistic models Bayesian classifier Statistical dependency Education 



This work has received financial support from the Ministry of Science and Innovation of Spain under grant TIN2014-56633-C3-1-R as well as from the Consellería de Cultura, Educación e Ordenación Universitaria (accreditation 2016–2019, ED431G/08) and the European Regional Development Fund (ERDF).


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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Grupo de Sistemas Inteligentes (GSI), Centro Singular de Investigación en Tecnologías de la Información (CITIUS)Universidad de Santiago de CompostelaSantiago de CompostelaSpain

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