Contrasting Prediction Methods for Early Warning Systems at Undergraduate Level

In this study, we investigate prediction methods for an early warning system for a large STEM undergraduate course. Recent studies have provided evidence in favour of adopting early warning systems as a means of identifying at-risk students. Many of these early warning systems rely on data from students' engagement with Learning Management Systems (LMSs). Our study examines eight prediction methods, and investigates the optimal time in a course to apply an early warning system. We present findings from a statistics university course which has a large proportion of resources on the LMS Blackboard and weekly continuous assessment. We identify weeks 5-6 of our course (half way through the semester) as an optimal time to implement an early warning system, as it allows time for the students to make changes to their study patterns whilst retaining reasonable prediction accuracy. Using detailed (fine-grained) variables, clustering and our final prediction method of BART (Bayesian Additive Regressive Trees) we are able to predict students' final grade by week 6 based on mean absolute error (MAE) to 6.5 percentage points. We provide our R code for implementation of the prediction methods used in a GitHub repository.