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Data-driven system to predict academic grades and dropout

Author

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  • Sergi Rovira
  • Eloi Puertas
  • Laura Igual

Abstract

Nowadays, the role of a tutor is more important than ever to prevent students dropout and improve their academic performance. This work proposes a data-driven system to extract relevant information hidden in the student academic data and, thus, help tutors to offer their pupils a more proactive personal guidance. In particular, our system, based on machine learning techniques, makes predictions of dropout intention and courses grades of students, as well as personalized course recommendations. Moreover, we present different visualizations which help in the interpretation of the results. In the experimental validation, we show that the system obtains promising results with data from the degree studies in Law, Computer Science and Mathematics of the Universitat de Barcelona.

Suggested Citation

  • Sergi Rovira & Eloi Puertas & Laura Igual, 2017. "Data-driven system to predict academic grades and dropout," PLOS ONE, Public Library of Science, vol. 12(2), pages 1-21, February.
    • Handle: RePEc:plo:pone00:0171207
    DOI: 10.1371/journal.pone.0171207
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    References listed on IDEAS

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    1. Carolien Van Soom & Vincent Donche, 2014. "Profiling First-Year Students in STEM Programs Based on Autonomous Motivation and Academic Self-Concept and Relationship with Academic Achievement," PLOS ONE, Public Library of Science, vol. 9(11), pages 1-13, November.
    2. Montmarquette, Claude & Mahseredjian, Sophie & Houle, Rachel, 2001. "The determinants of university dropouts: a bivariate probability model with sample selection," Economics of Education Review, Elsevier, vol. 20(5), pages 475-484, October.
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    Cited by:

    1. Miguel Angel Valles-Coral & Luis Salazar-Ramírez & Richard Injante & Edwin Augusto Hernandez-Torres & Juan Juárez-Díaz & Jorge Raul Navarro-Cabrera & Lloy Pinedo & Pierre Vidaurre-Rojas, 2022. "Density-Based Unsupervised Learning Algorithm to Categorize College Students into Dropout Risk Levels," Data, MDPI, vol. 7(11), pages 1-18, November.
    2. Neema Mduma, 2023. "Data Balancing Techniques for Predicting Student Dropout Using Machine Learning," Data, MDPI, vol. 8(3), pages 1-14, February.
    3. Fedriani Martel, Eugenio M. & Romano Paguillo, Inmaculada, 2017. "Análisis cualitativo comparativo difuso para determinar influencias entre variables socio-económicas y el rendimiento académico de los universitarios || Fuzzy-Set Qualitative Comparative Analysis to D," Revista de Métodos Cuantitativos para la Economía y la Empresa = Journal of Quantitative Methods for Economics and Business Administration, Universidad Pablo de Olavide, Department of Quantitative Methods for Economics and Business Administration, vol. 24(1), pages 250-269, Diciembre.

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