Before revision:  We separate the analysis into students in grade 5 and above, for which previous achievement may be used as a predictor; and students in grade 3, which must rely on family- and school-level predictors only. We train and compare a set of classifiers for reading and numeracy learning areas respectively. The classifiers achieve good predictive power in terms of area under the ROC curve, suggesting that it is feasible for schools to more accurately screen a large number of students for academic risk .
After revision:  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 .