Using predictive modelling to identify students at risk of poor university outcomes
This paper co-authored by Tim Maloney (CSDA co-director 2016-2019) outlines the use of predictive risk modelling tools to identify vulnerable students and understand factors that place these students at risk of non-completion and non-retention early in their university careers. Administrative data from the enrolment process is used to identify the factors contributing to these adverse outcomes.
Jia, P., & Maloney, T. (2015). Using predictive modelling to identify students at risk of poor university outcomes. Higher Education, 70(1), 127-149.