IDEAS home Printed from https://ideas.repec.org/a/gam/jstats/v4y2021i3p41-700d626644.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2571-905X/4/3/41/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2571-905X/4/3/41/
    Download Restriction: no
    ---><---

    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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Share Aiyed M Aldosari, 2020. "The Future of Higher Education in the Light of Artificial Intelligence Transformations," International Journal of Higher Education, Sciedu Press, vol. 9(3), pages 145-145, June.
    2. Nazera Emara & Nagla Ali & Othman Abu Khurma, 2023. "Adaptive Learning Framework (Alef) in UAE Public Schools from the Parents’ Perspective," Social Sciences, MDPI, vol. 12(5), pages 1-14, May.
    3. Ali, Omar & Murray, Peter A. & Momin, Mujtaba & Dwivedi, Yogesh K. & Malik, Tegwen, 2024. "The effects of artificial intelligence applications in educational settings: Challenges and strategies," Technological Forecasting and Social Change, Elsevier, vol. 199(C).
    4. Jesús Fernández-Bermejo Ruiz & Javier Dorado Chaparro & Maria José Santofimia Romero & Félix Jesús Villanueva Molina & Xavier del Toro García & Cristina Bolaños Peño & Henry Llumiguano Solano & Sara C, 2022. "Bedtime Monitoring for Fall Detection and Prevention in Older Adults," IJERPH, MDPI, vol. 19(12), pages 1-32, June.
    5. Kai Wang & Guo-Yuan Sang & Lan-Zi Huang & Shi-Hua Li & Jian-Wen Guo, 2023. "The Effectiveness of Educational Robots in Improving Learning Outcomes: A Meta-Analysis," Sustainability, MDPI, vol. 15(5), pages 1-16, March.
    6. Sadik Kamel Gharghan & Saleem Latteef Mohammed & Ali Al-Naji & Mahmood Jawad Abu-AlShaeer & Haider Mahmood Jawad & Aqeel Mahmood Jawad & Javaan Chahl, 2018. "Accurate Fall Detection and Localization for Elderly People Based on Neural Network and Energy-Efficient Wireless Sensor Network," Energies, MDPI, vol. 11(11), pages 1-32, October.
    7. Joyce de Souza Zanirato Maia & Ana Paula Arantes Bueno & Joao Ricardo Sato, 2023. "Applications of Artificial Intelligence Models in Educational Analytics and Decision Making: A Systematic Review," World, MDPI, vol. 4(2), pages 1-26, May.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jstats:v:4:y:2021:i:3:p:41-700:d:626644. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.