Prediction of Student Performance Through an Intelligent Hybrid Model

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

    Universidad de León

    León, España

    ROR https://ror.org/02tzt0b78

  2. 2 Universidade da Coruña
    info

    Universidade da Coruña

    La Coruña, España

    ROR https://ror.org/01qckj285

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

Publisher: Springer Suiza

ISBN: 978-3-030-29859-3

Year of publication: 2019

Pages: 710-721

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

Type: Conference paper

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Abstract

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.

Data source: Dialnet