The IAFOR Journal of Education
Volume 4 – Issue – 2

The IAFOR Journal of Education
Volume 4 – Issue – 2

The IAFOR Journal of Education
Volume 4 – Issue – 2

Universities are inundated with detailed applicant and enrolment data from a variety of sources.
However, for these data to be useful there is a need to convert them into strategic knowledge
and information for decision-making processes. This study uses predictive modelling to
identify at-risk adult learners in their first semester at SIM University, a Singapore University
that caters mainly to adult learners. Fourteen variables from the enrolment database were
considered as possible factors for the predictive model. To classify the at-risk students, various
algorithms were used such as a neural network and classification tree. The performances of the
different models were compared for sensitivity, specificity and accuracy indices. The model
chosen is a classification tree model that may be used to inform policy. The implications of
these results for identification of individuals in need of early intervention are discussed.
Keywords: predictive modelling; adult learners; higher education.

Bernard Montoneri