IDEAS home Printed from https://ideas.repec.org/p/arx/papers/1807.07215.html
   My bibliography  Save this paper

Machine Learning Classifiers Do Not Improve the Prediction of Academic Risk: Evidence from Australia

Author

Listed:
  • Sarah Cornell-Farrow
  • Robert Garrard

Abstract

Machine learning methods tend to outperform traditional statistical models at prediction. In the prediction of academic achievement, ML models have not shown substantial improvement over linear and logistic regression. So far, these results have almost entirely focused on college achievement, due to the availability of administrative datasets, and have contained relatively small sample sizes by ML standards. In this article we apply popular machine learning models to a large dataset ($n=2.2$ million) containing primary and middle school performance on NAPLAN, a test given annually to all Australian students in grades 3, 5, 7, and 9. We show that machine learning models do not outperform logistic regression for detecting students who will perform in the `below standard' band of achievement upon sitting their next test.

Suggested Citation

  • Sarah Cornell-Farrow & Robert Garrard, 2018. "Machine Learning Classifiers Do Not Improve the Prediction of Academic Risk: Evidence from Australia," Papers 1807.07215, arXiv.org, revised Feb 2019.
  • Handle: RePEc:arx:papers:1807.07215
    as

    Download full text from publisher

    File URL: http://arxiv.org/pdf/1807.07215
    File Function: Latest version
    Download Restriction: no

    References listed on IDEAS

    as
    1. Roland G. Fryer & Steven D. Levitt, 2004. "Understanding the Black-White Test Score Gap in the First Two Years of School," The Review of Economics and Statistics, MIT Press, vol. 86(2), pages 447-464, May.
    2. Elder, Todd & Jepsen, Christopher, 2014. "Are Catholic primary schools more effective than public primary schools?," Journal of Urban Economics, Elsevier, vol. 80(C), pages 28-38.
    3. Hui Zou & Trevor Hastie, 2005. "Addendum: Regularization and variable selection via the elastic net," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(5), pages 768-768, November.
    4. Cheti Nicoletti & Birgitta Rabe, 2013. "Inequality in Pupils' Test Scores: How Much do Family, Sibling Type and Neighbourhood Matter?," Economica, London School of Economics and Political Science, vol. 80(318), pages 197-218, April.
    5. Roland G. Fryer & Steven D. Levitt, 2010. "An Empirical Analysis of the Gender Gap in Mathematics," American Economic Journal: Applied Economics, American Economic Association, vol. 2(2), pages 210-240, April.
    6. Paul W. Miller & Derby Voon, 2014. "School outcomes in New South Wales and Queensland: a regression discontinuity approach," Education Economics, Taylor & Francis Journals, vol. 22(5), pages 427-448, October.
    7. Hui Zou & Trevor Hastie, 2005. "Regularization and variable selection via the elastic net," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(2), pages 301-320, April.
    8. Nghiem, Hong Son & Nguyen, Ha Trong & Khanam, Rasheda & Connelly, Luke B., 2015. "Does school type affect cognitive and non-cognitive development in children? Evidence from Australian primary schools," Labour Economics, Elsevier, vol. 33(C), pages 55-65.
    9. Fletcher, Jason & Kim, Taehoon, 2016. "The effects of changes in kindergarten entry age policies on educational achievement," Economics of Education Review, Elsevier, vol. 50(C), pages 45-62.
    10. Kevin Pugh & Gigi Foster, 2014. "Australia's National School Data and the ‘Big Data’ Revolution in Education Economics," Australian Economic Review, The University of Melbourne, Melbourne Institute of Applied Economic and Social Research, vol. 47(2), pages 258-268, June.
    Full references (including those not matched with items on IDEAS)

    More about this item

    NEP fields

    This paper has been announced in the following NEP Reports:

    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:arx:papers:1807.07215. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (arXiv administrators). General contact details of provider: http://arxiv.org/ .

    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 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.

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

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.