Prediction of Student Performance Through an Intelligent Hybrid Model

  1. Héctor Alaiz-Moretón 1
  2. José Antonio López Vázquez 2
  3. Héctor Quintián 2
  4. José-Luis Casteleiro-Roca 2
  5. Esteban Jove 2
  6. José Luis Calvo-Rolle 2
  1. 1 Universidad de León

    Universidad de León

    León, España


  2. 2 Universidade da Coruña

    Universidade da Coruña

    La Coruña, España


Hybrid Artificial Intelligent Systems. 14th International Conference, HAIS 2019: León, Spain, September 4–6, 2019. Proceedings
  1. Hilde Pérez García (coord.)
  2. Lidia Sánchez González (coord.)
  3. Manuel Castejón Limas (coord.)
  4. Héctor Quintián Pardo (coord.)
  5. Emilio Corchado Rodríguez (coord.)

Publisher: Springer Suiza

ISBN: 978-3-030-29859-3 978-3-030-29858-6

Year of publication: 2019

Pages: 710-721

Congress: Hybrid Artificial Intelligent Systems (14. 2019. León)

Type: Conference paper


The present work addresses the problem of low academic performance in engineering degree students. Models capable of predicting academic performance are generated through the application of several intelligent regression techniques to a dataset containing the official academic records of students of the engineering degree in the University of A Coruña. The global model, specifically the hybrid model based on K-means clustering, can predict the grade subject based on previous courses. In addition, an LDA (Linear Discriminant Analysis) has been implemented in order to identify the important features and visualize the classification clearly. Thus, the developed model makes it possible to estimate the academic performance of each student as well as the most important variables associated with it.