Interim identifi cation of �??at risk�?� students: A predictive model

Abstract

Mark I. K. Norrish, Pananghat A. Kumar, Thomas A. Heming

Identifying and supporting students who are academically at risk are an essential part of medical education. This study considers how well aspects of previous performance predict academic performance in the current year and whether it is possible to use a combination of previous and current performance to identify students who are academically “at risk” in the current year