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. arXiv:1612.05735v2 [math.HO]

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Student counselling services are at the forefront of providing mental health support to Irish Higher Education students. Since 1996, the Psychological Counsellors in Higher Education in Ireland (PCHEI) association, through their annual survey collection, has collected aggregate data for the sector. However, to identify national trends and effective interventions, a standardised non-aggregate sectoral approach to data collection is required. The Higher Education Authority funded project, 3SET, builds on the PCHEI survey through the development of a national database. In this paper, we outline the steps followed in developing the database, identify the parties involved at each stage and contrast the approach taken to the development of similar databases. Important factors shaping the development have been the autonomy of counselling services, compliance with General Data Protection Regulation, and the involvement of practitioners. This is an ongoing project with the long-term sustainability of the database being a primary objective.