Comparative of Clustering Techniques for Academic Advice and Performance Measurement

  1. Maite García-Ordás 1
  2. José Antonio López Vázquez 2
  3. Héctor Alaiz Moretón 1
  4. José-Luis Casteleiro-Roca 1
  5. David Yeregui Marcos del Blanco 3
  6. Roberto Casado Vara 4
  7. 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


  3. 3 Universidad IE

    Universidad IE

    Segovia, España


  4. 4 Universidad de Salamanca

    Universidad de Salamanca

    Salamanca, España


The 11th International Conference on EUropean Transnational Educational: (ICEUTE 2020)
  1. Álvaro Herrero Cosío (ed. lit.)
  2. Carlos Cambra Baseca (ed. lit.)
  3. Daniel Urda Muñoz (ed. lit.)
  4. Javier Sedano Franco (ed. lit.)
  5. Héctor Quintián Pardo (ed. lit.)
  6. Emilio Santiago Corchado Rodríguez (ed. lit.)

Publisher: Springer Suiza

ISBN: 3-030-57798-8 3-030-57799-6

Year of publication: 2021

Pages: 215-226

Congress: International Conference on EUropean Transnational Educational (ICEUTE) (11. 2020. Burgos)

Type: Conference paper


This article presents an innovative proposal for improving personalized student performance counselling. The methodology implemented applies clustering techniques in order to obtain group profiles of students with similar features. The research has been performed utilizing anonymized real academic grades from student data sets of the Polytechnic School of the University of A Coruñaa. The ultimate purpose for the proposed tool is to be dynamic and adaptive to different data sets. Therefore, only the most representative, universal variables are considered. Overall, three techniques have been evaluated for clustering, with two additional ones for dimensional reduction with very promising results.