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Interim identifi cation of “at risk” students: A predictive model

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

Objective: 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. Methods: Regression analysis was used
to investigate how previous academic performance influences current performance in medical students at
a mid-point in their studies (year 4 of 7). To identify students who are academically “at risk” in the current
year a risk prediction model was developed on the basis of a combination of current and past performance
variables. Results: Cumulative grade point average and previous failures were identified as significant macrolevel
predictors of performance, and in addition several subject-by-subject level predictors were identified as
independently significant. This risk prediction model accurately predicted “at risk” students, with a high degree of
sensitivity and specificity, as shown by the non-progression of 88% of the students who were identified as being
‘at risk’. Conclusions: By considering the academic content of courses, the development of transferable skills
and how recently a course was taken, it is possible to accurately predict academic performance. It is possible
to identify students who are academically “at risk” by developing a predictive model using a combination of
current and previous variables. Therefore, non-completion rates in medical schools could be reduced through
the timely introduction of specific and targeted remedial interventions for “at risk” students.

Key words: Academic performance, at risk, progression, medical education

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Journal of Contemporary Medical Education


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