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

Sarah Cornell-Farrow and Robert Garrard ()

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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.

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Date: 2018-07, Revised 2019-02
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