Comparative of Clustering Techniques for Academic Advice and Performance Measurement

• María Teresa García-Ordás ^[1] ; José Antonio López-Vázquez ^[2] ; Héctor Alaiz-Moretón ^[1] ; José Luis Casteleiro-Roca ^[1] ; David Yeregui Marcos del Blanco ^[3] ; Roberto Casado-Vara ^[4] ; José Luis Calvo-Rolle ^[2]
  1. [1] Universidad de León

     Universidad de León

     León, España

     
  2. [2] Universidade da Coruña

     Universidade da Coruña

     A Coruña, España

     
  3. [3] Universidad IE

     Universidad IE

     Segovia, España

     
  4. [4] Universidad de Salamanca

     Universidad de Salamanca

     Salamanca, España

     

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• Localización: The 11th International Conference on EUropean Transnational Educational: (ICEUTE 2020) / Álvaro Herrero Cosío (ed. lit.), Carlos Cambra Baseca (ed. lit.), Daniel Urda Muñoz (ed. lit.), Javier Sedano Franco (ed. lit.), Héctor Quintián Pardo (ed. lit.), Emilio Santiago Corchado Rodríguez (ed. lit.), 2021, ISBN 3-030-57798-8, págs. 215-226
• Idioma: inglés
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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.