Journal Article


Andrew Parnell
Maria Meehan
Emma Howard


Computer Science

prediction patterns neural networks undergraduate implementation support vector machine learning management system university

Contrasting prediction methods for early warning systems at undergraduate level (2018)

Abstract Recent studies have provided evidence in favour of adopting early warning systems as a means of identifying atrisk students. Our study examines eight prediction methods, and investigates the optimal time in a course to apply such a system. We present findings from a statistics university course which has weekly continuous assessment and a large proportion of resources on the Learning Management System Blackboard. We identify weeks 5–6 (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 while retaining reasonable prediction accuracy. Using detailed variables, clustering and our final prediction method of BART (Bayesian Additive Regressive Trees) we can predict students' final mark by week 6 based on mean absolute error to 6.5 percentage points. We provide our R code for implementation of the prediction methods used in a GitHub repository. Abbreviations: Bayesian Additive Regressive Trees (BART); Random Forests (RF); Principal Components Regression (PCR); Multivariate Adaptive Regression Splines (Splines); K-Nearest Neighbours (KNN); Neural Networks (NN) and; Support Vector Machine (SVM)
Collections Ireland -> Maynooth University -> Type = Article
Ireland -> Maynooth University -> Academic Unit = Faculty of Science and Engineering: Research Institutes: Hamilton Institute
Ireland -> Maynooth University -> Status = Published

Full list of authors on original publication

Andrew Parnell, Maria Meehan, Emma Howard

Experts in our system

Andrew Parnell
Maynooth University
Total Publications: 45
Maria Meehan
Maynooth University
Total Publications: 9
Emma Howard
University College Dublin
Total Publications: 5