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Predicting Students' Progression in Higher Education by Using the Random Forest Algorithm

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  • Julie Hardman
  • Alberto Paucar‐Caceres
  • Alan Fielding

Abstract

This paper proposes the use of data available at Manchester Metropolitan University to assess the variables that can best predict student progression. We combine virtual learning environment (VLE) and management information systems student records datasets and apply the Random Forest (RF) algorithm to ascertain which variables can best predict students' progression. RF was deemed useful in this case because of the large amount of data available for analysis. The paper reports on the initial findings for data available in the period 2007–2008. Results seem to indicate that variables such as students' time of day usage, the last time students access the VLE and the number of document hits by staff are the best predictors of student progression. The paper contributes to VLE evaluation and highlights the usefulness of RF, a technique initially developed in the field of biology, in evaluating an educational and learning environment. Copyright © 2012 John Wiley & Sons, Ltd.

Suggested Citation

  • Julie Hardman & Alberto Paucar‐Caceres & Alan Fielding, 2013. "Predicting Students' Progression in Higher Education by Using the Random Forest Algorithm," Systems Research and Behavioral Science, Wiley Blackwell, vol. 30(2), pages 194-203, March.
    • Handle: RePEc:bla:srbeha:v:30:y:2013:i:2:p:194-203
    DOI: 10.1002/sres.2130
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    Cited by:

    1. Siqing Shan & Cangyan Li & Jihong Shi & Li Wang & Huali Cai, 2014. "Impact of Effective Communication, Achievement Sharing and Positive Classroom Environments on Learning Performance," Systems Research and Behavioral Science, Wiley Blackwell, vol. 31(3), pages 471-482, May.

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