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Analysis of Student Achievement Scores via Cluster Analysis

  • Jiménez Chaves, Viviana E. [3] ; Miguel García-Torres [1] ; David Becerra Alonso [2] ; Francisco Gómez-Vela [1] ; Federico Divina [1] ; Vázquez-Noguera, José L. [3]
    1. [1] Universidad Pablo de Olavide

      Universidad Pablo de Olavide

      Sevilla, España

    2. [2] Universidad Loyola Andalucía

      Universidad Loyola Andalucía

      Sevilla, España

    3. [3] Universidad Americana (Asunción, Paraguay)
  • Localización: The 11th International Conference on EUropean Transnational Educational: (ICEUTE 2020) / Álvaro Herrero Cosío (ed. lit.) Árbol académico, Carlos Cambra Baseca (ed. lit.) Árbol académico, Daniel Urda Muñoz (ed. lit.) Árbol académico, Javier Sedano Franco (ed. lit.) Árbol académico, Héctor Quintián Pardo (ed. lit.) Árbol académico, Emilio Santiago Corchado Rodríguez (ed. lit.) Árbol académico, 2021, ISBN 3-030-57798-8, págs. 399-408
  • Idioma: inglés
  • Texto completo no disponible (Saber más ...)
  • Resumen
    • 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 inthis physics course.


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