IDEAS home Printed from https://ideas.repec.org/a/ibn/hesjnl/v10y2020i4p12.html
   My bibliography  Save this article

A Machine Learning-Based Computational System Proposal Aiming at Higher Education Dropout Prediction

Author

Listed:
  • Maria do Carmo Nicoletti
  • Osvaldo Luiz de Oliveira

Abstract

In the literature related to higher education, the concept of dropout has been approached from several perspectives and, over the years, its definition has been influenced by the use of diversified semantic interpretations. In a general higher education environment dropout can be broadly characterized as the act of a student engaged in a course leaving the educational institution without finishing the course. This paper describes the proposal of the architecture of a computational system, PDE (Predicting Dropout Events), based on machine learning (ML) algorithms and specifically designed for predicting dropout events in a higher level educational environment. PDE’s main subsystem implements a group of instance-based learning (IBL) algorithms which, taking into account a particular university-course environment, and based on log files containing descriptions of previous dropouts events, is capable to predict when a student already engaged in the course, is prone to dropout, so preventive measures could be quickly implemented.

Suggested Citation

  • Maria do Carmo Nicoletti & Osvaldo Luiz de Oliveira, 2020. "A Machine Learning-Based Computational System Proposal Aiming at Higher Education Dropout Prediction," Higher Education Studies, Canadian Center of Science and Education, vol. 10(4), pages 1-12, December.
    • Handle: RePEc:ibn:hesjnl:v:10:y:2020:i:4:p:12
    as

    Download full text from publisher

    File URL: http://www.ccsenet.org/journal/index.php/hes/article/download/0/0/43696/45905
    Download Restriction: no
    File URL: http://www.ccsenet.org/journal/index.php/hes/article/view/0/43696
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Vincent Tinto, 1997. "Classrooms as Communities," The Journal of Higher Education, Taylor & Francis Journals, vol. 68(6), pages 599-623, November.
    2. Dario Sansone, 2019. "Beyond Early Warning Indicators: High School Dropout and Machine Learning," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 81(2), pages 456-485, April.
    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. Aina, Carmen & Baici, Eliana & Casalone, Giorgia & Pastore, Francesco, 2018. "The Economics of University Dropouts and Delayed Graduation: A Survey," IZA Discussion Papers 11421, Institute of Labor Economics (IZA).
    2. Halim,Daniel Zefanya & Seetahul,Suneha, 2023. "Why Do People Move ? A Data-Driven Approach to Identifying and PredictingGender-Specific Aspirations to Migrate," Policy Research Working Paper Series 10396, The World Bank.
    3. Delogu, Marco & Lagravinese, Raffaele & Paolini, Dimitri & Resce, Giuliano, 2024. "Predicting dropout from higher education: Evidence from Italy," Economic Modelling, Elsevier, vol. 130(C).
    4. Filmer,Deon P. & Nahata,Vatsal & Sabarwal,Shwetlena, 2021. "Preparation, Practice, and Beliefs : A Machine Learning Approach to Understanding Teacher Effectiveness," Policy Research Working Paper Series 9847, The World Bank.
    5. Hazal Colak Oz & Çiçek Güven & Gonzalo Nápoles, 2023. "School dropout prediction and feature importance exploration in Malawi using household panel data: machine learning approach," Journal of Computational Social Science, Springer, vol. 6(1), pages 245-287, April.
    6. Dario Sansone & Anna Zhu, 2023. "Using Machine Learning to Create an Early Warning System for Welfare Recipients," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 85(5), pages 959-992, October.
    7. Isphording, Ingo E. & Raabe, Tobias, 2019. "Early Identification of College Dropouts Using Machine-Learning: Conceptual Considerations and an Empirical Example," IZA Research Reports 89, Institute of Labor Economics (IZA).
    8. Maria do Carmo Nicoletti, 2019. "Revisiting the Tinto's Theoretical Dropout Model," Higher Education Studies, Canadian Center of Science and Education, vol. 9(3), pages 52-64, September.
    9. Markus Frölich & Martin Huber, 2014. "Treatment Evaluation With Multiple Outcome Periods Under Endogeneity and Attrition," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 109(508), pages 1697-1711, December.
    10. Maximiliano Machado, 2020. "Las Tutorías Entre Pares y sus efectos en el desempeño de los estudiantes," Documentos de Trabajo (working papers) 20-16, Instituto de Economía - IECON.
    11. Aina, Carmen & Baici, Eliana & Casalone, Giorgia & Pastore, Francesco, 2022. "The determinants of university dropout: A review of the socio-economic literature," Socio-Economic Planning Sciences, Elsevier, vol. 79(C).
    12. Arulampalam, Wiji & Naylor, Robin A. & Smith, Jeremy P., 2005. "Effects of in-class variation and student rank on the probability of withdrawal: cross-section and time-series analysis for UK university students," Economics of Education Review, Elsevier, vol. 24(3), pages 251-262, June.
    13. Hanwei Li, 2017. "Academic Integration of Mainland Chinese Students in Germany," Social Inclusion, Cogitatio Press, vol. 5(1), pages 80-92.
    14. Montorsi, Carlotta & Fusco, Alessio & Van Kerm, Philippe & Bordas, Stéphane P.A., 2024. "Predicting depression in old age: Combining life course data with machine learning," Economics & Human Biology, Elsevier, vol. 52(C).
    15. Giorgio Di Pietro, 2006. "Regional labour market conditions and university dropout rates: Evidence from Italy," Regional Studies, Taylor & Francis Journals, vol. 40(6), pages 617-630.
    16. Masserini, Lucio & Bini, Matilde, 2021. "Does joining social media groups help to reduce students’ dropout within the first university year?," Socio-Economic Planning Sciences, Elsevier, vol. 73(C).
    17. Togara Warinda, 2013. "Accounting Students’ Evaluation of Internship Experiences from a Skills Perspective," International Journal of Asian Social Science, Asian Economic and Social Society, vol. 3(3), pages 783-799, March.
    18. Jonas Forsman & Richard P Mann & Cedric Linder & Maartje van den Bogaard, 2014. "Sandbox University: Estimating Influence of Institutional Action," PLOS ONE, Public Library of Science, vol. 9(7), pages 1-9, July.
    19. Arco-Tirado, J.L. & Fernández-Martín, F. & Ramos-García, A.M. & Littvay, L. & Villoria, J. & Naranjo, J.A., 2018. "A counterfactual impact evaluation of a bilingual program on students’ grade point average at a spanish university," Evaluation and Program Planning, Elsevier, vol. 68(C), pages 81-89.
    20. E. Opdecam & P. Everaert & H. Van Keer & F. Buysschaert, 2012. "The effect of team learning on student profile and student performance in accounting education," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 12/774, Ghent University, Faculty of Economics and Business Administration.

    More about this item

    JEL classification:

    • R00 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General - - - General
    • Z0 - Other Special Topics - - General

    Statistics

    Access and download statistics

    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:ibn:hesjnl:v:10:y:2020:i:4:p:12. 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: Canadian Center of Science and Education (email available below). General contact details of provider: https://edirc.repec.org/data/cepflch.html .

    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.