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Learning Time Acceleration in Support Vector Regression: A Case Study in Educational Data Mining

Author

Listed:
  • Jonatha Sousa Pimentel

    (Statistics Department, Federal University of Bahia, Salvador 40170-110, Brazil)

  • Raydonal Ospina

    (Statistics Department, CASTLab, Federal University of Pernambuco, Recife 50670-901, Brazil)

  • Anderson Ara

    (Statistics Department, Federal University of Bahia, Salvador 40170-110, Brazil)

Abstract

The development of a country involves directly investing in the education of its citizens. Learning analytics/educational data mining (LA/EDM) allows access to big observational structured/unstructured data captured from educational settings and relies mostly on machine learning algorithms to extract useful information. Support vector regression (SVR) is a supervised statistical learning approach that allows modelling and predicts the performance tendency of students to direct strategic plans for the development of high-quality education. In Brazil, performance can be evaluated at the national level using the average grades of a student on their National High School Exams (ENEMs) based on their socioeconomic information and school records. In this paper, we focus on increasing the computational efficiency of SVR applied to ENEM for online requisitions. The results are based on an analysis of a massive data set composed of more than five million observations, and they also indicate computational learning time savings of more than 90%, as well as providing a prediction of performance that is compatible with traditional modeling.

Suggested Citation

  • Jonatha Sousa Pimentel & Raydonal Ospina & Anderson Ara, 2021. "Learning Time Acceleration in Support Vector Regression: A Case Study in Educational Data Mining," Stats, MDPI, vol. 4(3), pages 1-19, August.
    • Handle: RePEc:gam:jstats:v:4:y:2021:i:3:p:41-700:d:626644
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    References listed on IDEAS

    as
    1. Ilkka Tuomi, 2018. "The Impact of Artificial Intelligence on Learning, Teaching, and Education," JRC Research Reports JRC113226, Joint Research Centre.
    2. Omar Aziz & Jochen Klenk & Lars Schwickert & Lorenzo Chiari & Clemens Becker & Edward J Park & Greg Mori & Stephen N Robinovitch, 2017. "Validation of accuracy of SVM-based fall detection system using real-world fall and non-fall datasets," PLOS ONE, Public Library of Science, vol. 12(7), pages 1-11, July.
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