Comparative of Clustering Techniques for Academic Advice and Performance Measurement

  1. María Teresa 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
    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

  3. 3 Universidad IE
    info

    Universidad IE

    Segovia, España

    ROR https://ror.org/02jjdwm75

  4. 4 Universidad de Salamanca
    info

    Universidad de Salamanca

    Salamanca, España

    ROR https://ror.org/02f40zc51

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

Editorial: Springer Suiza

ISBN: 3-030-57798-8,  3-030-57799-6

Año de publicación: 2021

Páginas: 215-226

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

Tipo: Aportación congreso

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Resumen

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.

Fuente de los datos: Dialnet