Random forest missing data imputation methods: Implications for predicting at-risk students

Abstract

In the field of higher education, predicting students At-Risk of failing is crucial since they could then be recommended for various interventions. These predictions are made on real world datasets that most likely have various missing data. Addressing this missing data could have substantial affects on the eventual At-Risk predictions. In this study we address the missing data problem with recently developed missing data imputation techniques not currently seen in the relevant literature. These techniques include multivariate imputation by chained equations (MICE) and missForest. We found that MICE does not perform better than simple listwise-deletion. However, missForest is shown to substantially improve predictive performance. This is important since it implies that any subsequent machine learning predictions on students At-Risk of failing are potentially much better.