Howard, Emma and Meehan, Maria and Parnell, Andrew
(2018)
Contrasting prediction methods for early warning systems at undergraduate level.
The Internet and Higher Education, 37.
pp. 66-75.
ISSN 1096-7516
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)
| Item Type: |
Article
|
| Keywords: |
Learning analytics;
Early warning systems;
Undergraduate education;
Prediction modelling; |
| Academic Unit: |
Faculty of Science and Engineering > Research Institutes > Hamilton Institute |
| Item ID: |
13275 |
| Identification Number: |
https://doi.org/10.1016/j.iheduc.2018.02.001 |
| Depositing User: |
Andrew Parnell
|
| Date Deposited: |
24 Sep 2020 14:42 |
| Journal or Publication Title: |
The Internet and Higher Education |
| Publisher: |
Elsevier |
| Refereed: |
Yes |
| URI: |
|
| Use Licence: |
This item is available under a Creative Commons Attribution Non Commercial Share Alike Licence (CC BY-NC-SA). Details of this licence are available
here |
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