Selecting Relevant Educational Attributes for Predicting Students’ Academic Performance

  • Abir Abid
  • Ilhem Kallel
  • Ignacio J. Blanco
  • Mounir Benayed
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 736)

Abstract

Predicting students’ academic performance is one of the oldest and most popular applications of educational data mining. It helps to estimate the unknown evaluation of a student’s performance. However, a huge amount of data with different formats and from multiple sources may contain a large number of features supposed as not-relevant that could influence the prediction results. The main objective of this paper is to improve the effectiveness of a predictive model for students’ academic performance. For this purpose, we propose a methodology to carry out a comparative study for evaluating the influence of feature selection techniques on the prediction of students’ academic performance. In our study, F-measure parameter is used to evaluate the effectiveness of the selected techniques. Two real data sources are used in this work, Mathematics and language courses. The outcomes are compared and discussed in order to identify the technique that has the best influence for an accurate predictive model.

Notes

Acknowledgment

The authors express thanks to the Erasmus+ project for funding the research reported under the Grant Agreement number 2015-1-ES01-K107-015469.

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Abir Abid
    • 1
  • Ilhem Kallel
    • 1
    • 2
  • Ignacio J. Blanco
    • 3
  • Mounir Benayed
    • 1
    • 4
  1. 1.REGIM-Lab: Research Groups in Intelligent Machines, ENISUniversity of SfaxSfaxTunisia
  2. 2.ISIMS: Higher Institute of Computer Science and Multimedia of SfaxSfaxTunisia
  3. 3.University of GranadaGranadaSpain
  4. 4.Computer Science and Communications Department, Faculty of Sciences of SfaxUniversity of SfaxSfaxTunisia

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