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Comparative of Clustering Techniques for Academic Advice and Performance Measurement

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The 11th International Conference on EUropean Transnational Educational (ICEUTE 2020) (ICEUTE 2020)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1266))

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Abstract

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 Coruña. 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.

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Author information

Authors and Affiliations

  1. Department of Electrical, Systems and Automatic Engineering, University of León, Campus de Vegazana s/n, 24071, León, Spain

    María Teresa García-Ordás & Héctor Alaiz-Moretón

  2. Department of Industrial Engineering, University of A Coruña, CTC, CITIC, Avda. 19 de febrero s/n, 15405, Ferrol, A Coruña, Spain

    José Antonio López-Vázquez, José Luis Casteleiro-Roca & José Luis Calvo-Rolle

  3. IE University, Maria de Molina, 28006, Madrid, Spain

    David Yeregui Marcos del Blanco

  4. BISITE Research Group, IoT Digital Innovation Hub, Edificio Multiusos I+D+i, University of Salamanca, 37007, Salamanca, Spain

    Roberto Casado-Vara

Authors
  1. María Teresa García-Ordás
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  2. José Antonio López-Vázquez
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  3. Héctor Alaiz-Moretón
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  4. José Luis Casteleiro-Roca
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  5. David Yeregui Marcos del Blanco
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  6. Roberto Casado-Vara
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  7. José Luis Calvo-Rolle
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Corresponding author

Correspondence to José Luis Casteleiro-Roca .

Editor information

Editors and Affiliations

  1. Grupo de Inteligencia Computacional Aplicada (GICAP), Departamento de Ingeniería Informática, Escuela Politécnica Superior, Universidad de Burgos, Burgos, Spain

    Álvaro Herrero

  2. Grupo de Inteligencia Computacional Aplicada (GICAP), Departamento de Ingeniería Informática, Escuela Politécnica Superior, Universidad de Burgos, Burgos, Spain

    Carlos Cambra

  3. Grupo de Inteligencia Computacional Aplicada (GICAP), Departamento de Ingeniería Informática, Escuela Politécnica Superior, Universidad de Burgos, Burgos, Spain

    Daniel Urda

  4. Technological Institute of Castilla y León, Burgos, Spain

    Javier Sedano

  5. Department of Industrial Engineering, University of A Coruña, La Coruña, Spain

    Héctor Quintián

  6. University of Salamanca, Salamanca, Spain

    Emilio Corchado

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García-Ordás, M.T. et al. (2021). Comparative of Clustering Techniques for Academic Advice and Performance Measurement. In: Herrero, Á., Cambra, C., Urda, D., Sedano, J., Quintián, H., Corchado, E. (eds) The 11th International Conference on EUropean Transnational Educational (ICEUTE 2020). ICEUTE 2020. Advances in Intelligent Systems and Computing, vol 1266. Springer, Cham. https://doi.org/10.1007/978-3-030-57799-5_23

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