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Predicting Academic Performance in Mathematics Using Machine Learning Algorithms

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Technologies and Innovation (CITI 2022)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1658))

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Abstract

Several factors, directly and indirectly, influence students’ performance in their various activities. Children and adolescents in the education process generate enormous data that could be analyzed to promote changes in current educational models. Therefore, this study proposes using machine learning algorithms to evaluate the variables influencing mathematics achievement. Three models were developed to identify behavioral patterns such as passing or failing achievement. On the one hand, numerical variables such as grades in exams of other subjects or entrance to higher education and categorical variables such as institution financing, student’s ethnicity, and gender, among others, are analyzed. The methodology applied was based on CRISP-DM, starting with the debugging of the database with the support of the Python library, Sklearn. The algorithms used are Decision Tree (DT), Naive Bayes (NB), and Random Forest (RF), the last one being the best, with 92% accuracy, 98% recall, and 97% recovery. As mentioned above, the attributes that best contribute to the model are the entrance exam score for higher education, grade exam, and achievement scores in linguistic, scientific, and social studies domains. This confirms the existence of data that help to develop models that can be used to improve curricula and regional education regulations.

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Notes

  1. 1.

    https://educacion.gob.ec/datos-abiertos/.

  2. 2.

    https://colab.research.google.com/notebooks/welcome.ipynb?hl=es.

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Acknowledgment

Universidad Tecnológica Indoamérica for its support and financing under the “Big data analysis and its impact on society, education and industry” project.

Author information

Authors and Affiliations

  1. Carrera de Psicología, Facultad de Ciencias Humanas y de la Salud, Universidad Tecnológica Indoamérica, Ambato, 180103, Ecuador

    Carlos Alberto Espinosa-Pinos

  2. Centro de Investigaciones de Ciencias Humanas y de la Educación CICHE, Universidad Indoa-mérica, Ambato, 180103, Ecuador

    Ignacio Ayala-Chauvín

  3. Carrera de Ingeniería Industrial, Facultad de Ingeniería y Tecnologías de la Información y la Comunicación, Universidad Indoamérica, Ambato, 180103, Ecuador

    Jorge Buele

  4. Department of Electronic Engineering and Communications, University of Zaragoza, 44003, Teruel, Spain

    Jorge Buele

Authors
  1. Carlos Alberto Espinosa-Pinos
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  2. Ignacio Ayala-Chauvín
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  3. Jorge Buele
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Corresponding author

Correspondence to Carlos Alberto Espinosa-Pinos .

Editor information

Editors and Affiliations

  1. Universidad de Murcia, Murcia, Spain

    Rafael Valencia-García

  2. Universidad Agraria del Ecuador, Guayaquil, Ecuador

    Martha Bucaram-Leverone

  3. Universidad Agraria del Ecuador, Guayaquil, Ecuador

    Javier Del Cioppo-Morstadt

  4. Universidad Agraria del Ecuador, Guayaquil, Ecuador

    Néstor Vera-Lucio

  5. Universidad Agraria del Ecuador, Guayaquil, Ecuador

    Emma Jácome-Murillo

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Espinosa-Pinos, C.A., Ayala-Chauvín, I., Buele, J. (2022). Predicting Academic Performance in Mathematics Using Machine Learning Algorithms. In: Valencia-García, R., Bucaram-Leverone, M., Del Cioppo-Morstadt, J., Vera-Lucio, N., Jácome-Murillo, E. (eds) Technologies and Innovation. CITI 2022. Communications in Computer and Information Science, vol 1658. Springer, Cham. https://doi.org/10.1007/978-3-031-19961-5_2

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  • DOI: https://doi.org/10.1007/978-3-031-19961-5_2

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-19960-8

  • Online ISBN: 978-3-031-19961-5

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