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

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 20072008. 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 (c) 2012 John Wiley & Sons, Ltd.