  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.
