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Predicting Students' Progression in Higher Education by Using the Random Forest Algorithm
Author(s) -
Hardman Julie,
PaucarCaceres Alberto,
Fielding Alan
Publication year - 2012
Publication title -
systems research and behavioral science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.371
H-Index - 45
eISSN - 1099-1743
pISSN - 1092-7026
DOI - 10.1002/sres.2130
Subject(s) - random forest , computer science , metropolitan area , field (mathematics) , learning management , machine learning , mathematics education , algorithm , psychology , data science , artificial intelligence , medical education , world wide web , mathematics , medicine , pathology , pure mathematics
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.