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    Contrasting prediction methods for early warning systems at undergraduate level


    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

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    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:

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