Identification of Early Predictors of Adult Learners’ Academic Performance in Higher Education

• Main Author: Sylvia Chong Nguik Yin
• Format: Article
• Language: English
• Published: The International Academic Forum 2016-09-01
• Series: IAFOR Journal of Education
• Subjects: predictive modelling; adult learners; higher education
• Online Access: http://iafor.org/wp-content/uploads/2016/09/Identification-of-Early-Predictors-of-Adult-Learners%E2%80%99Academic-Performance-in-Higher-Education.pdf

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.