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Analysis of Student Achievement Scores: A Machine Learning Approach

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International Joint Conference: 12th International Conference on Computational Intelligence in Security for Information Systems (CISIS 2019) and 10th International Conference on EUropean Transnational Education (ICEUTE 2019) (CISIS 2019, ICEUTE 2019)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 951))

Abstract

Educational Data Mining (EDM) is an emerging discipline of increasing interest due to several factors, such as the adoption of learning management systems in education environment. In this work we analyze the predictive power of continuous evaluation activities with respect the overall student performance in physics course at Universidad Loyola Andalucíıa, in Seville, Spain. Such data was collected during the fall semester of 2018 and we applied several classification algorithms, as well as feature selection strategies. Results suggest that several activities are not really relevant and, so, machine learning techniques may be helpful to design new relevant and non-redundant activities for enhancing student knowledge acquisition in physics course. These results may be extrapolated to other courses.

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Author information

Authors and Affiliations

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

    Miguel García-Torres, Francisco A. Gómez-Vela, Federico Divina & Francisco Martínez-Álvarez

  2. Universidad Loyola Andalucía, Seville, Spain

    David Becerra-Alonso & Isabel López Cobo

Authors
  1. Miguel García-Torres
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  2. David Becerra-Alonso
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  3. Francisco A. Gómez-Vela
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  4. Federico Divina
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  5. Isabel López Cobo
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  6. Francisco Martínez-Álvarez
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Corresponding author

Correspondence to Miguel García-Torres .

Editor information

Editors and Affiliations

  1. Data Science and Big Data Lab, Pablo de Olavide University, Seville, Spain

    Francisco Martínez Álvarez

  2. Data Science and Big Data Lab, Pablo de Olavide University, Seville, Spain

    Alicia Troncoso Lora

  3. University of Salamanca, Salamanca, Spain

    José António Sáez Muñoz

  4. Department of Industrial Engineering, University of A Coruña, A Coruña, Spain

    Héctor Quintián

  5. University of Salamanca, Salamanca, Spain

    Emilio Corchado

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García-Torres, M., Becerra-Alonso, D., Gómez-Vela, F.A., Divina, F., Cobo, I.L., Martínez-Álvarez, F. (2020). Analysis of Student Achievement Scores: A Machine Learning Approach. In: Martínez Álvarez, F., Troncoso Lora, A., Sáez Muñoz, J., Quintián, H., Corchado, E. (eds) International Joint Conference: 12th International Conference on Computational Intelligence in Security for Information Systems (CISIS 2019) and 10th International Conference on EUropean Transnational Education (ICEUTE 2019). CISIS ICEUTE 2019 2019. Advances in Intelligent Systems and Computing, vol 951. Springer, Cham. https://doi.org/10.1007/978-3-030-20005-3_28

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