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Analysis of Student Achievement Scores via Cluster Analysis

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Book cover 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

In education, the overall performance of every student is an important issue when assessing the quality of teaching. However, in the traditional educational system not all students have the same opportunity to develop their academic skills in an efficient way. Different teaching techniques have been proposed to adapt the learning process to the student profile. In this work, we analyze the profile of students according to their performance on academic activities and taking into account two different evaluation systems: work-based assessment and knowledge-based assessment. To this aim, data was collected during the fall semester of 2019 from a physics course at Universidad Loyola Andalucía, in Seville, Spain. In order to study the student profiles, a clustering approach combined with supervised feature selection was applied. Results suggest that two student profiles are clearly distinguished according to their performance in the course in both evaluation approaches. These two profiles correspond to students that pass and fail the course. The output of the analysis also indicates that there are redundant and/or irrelevant features. Therefore, machine learning techniques may be helpful for the design of effective activities to enhance the student learning process in this physics course.

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Authors and Affiliations

  1. Division of Computer Science, Universidad Pablo de Olavide, 41013, Seville, Spain

    Miguel García-Torres, Francisco Gómez-Vela & Federico Divina

  2. Universidad Loyola Andalucía, Seville, Spain

    David Becerra Alonso

  3. Universidad Americana, Asunción, Paraguay

    Viviana E. Jiménez Chaves & José L. Vázquez-Noguera

Authors
  1. Viviana E. Jiménez Chaves
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  2. Miguel García-Torres
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  3. David Becerra Alonso
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  4. Francisco Gómez-Vela
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  5. Federico Divina
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  6. José L. Vázquez-Noguera
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Corresponding author

Correspondence to Miguel García-Torres .

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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Chaves, V.E.J., García-Torres, M., Alonso, D.B., Gómez-Vela, F., Divina, F., Vázquez-Noguera, J.L. (2021). Analysis of Student Achievement Scores via Cluster Analysis. 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_41

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