Citation: Yin, S. C. N. (2016). Identification of Early Predictors of Adult Learners’ Academic Performance in Higher Education. IAFOR Journal of Education, 4(2). https://doi.org/10.22492/ije.4.2.01
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.
predictive modelling, adult learners, higher education