  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 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 ($n=1.2$ million) containing primary and middle school performance on a
standardized test given annually to Australian students. 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, even in a large-$n$ setting.
