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


  • Sarah Cornell-Farrow
  • Robert Garrard


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.

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

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    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.
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