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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)

Abstract

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.

Keywords

Probabilistic models Bayesian classifier Statistical dependency Education 

Notes

Acknowledgments

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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