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Predicting performance at medical school: can we identify at-risk students?

Authors Shaban S, McLean M

Published 17 May 2011 Volume 2011:2 Pages 139—148


Review by Single anonymous peer review

Peer reviewer comments 2

Sami Shaban, Michelle McLean
Department of Medical Education, Faculty of Medicine and Health Sciences, United Arab Emirates University, Al Ain, United Arab Emirates

Background: The purpose of this study was to examine the predictive potential of multiple indicators (eg, preadmission scores, unit, module and clerkship grades, course and examination scores) on academic performance at medical school, with a view to identifying students at risk.
Methods: An analysis was undertaken of medical student grades in a 6-year medical school program at the Faculty of Medicine and Health Sciences, United Arab Emirates University, Al Ain, United Arab Emirates, over the past 14 years.
Results: While high school scores were significantly (P < 0.001) correlated with the final integrated examination, predictability was only 6.8%. Scores for the United Arab Emirates university placement assessment (Common Educational Proficiency Assessment) were only slightly more promising as predictors with 14.9% predictability for the final integrated examination. Each unit or module in the first four years was highly correlated with the next unit or module, with 25%–60% predictability. Course examination scores (end of years 2, 4, and 6) were significantly correlated (P < 0.001) with the average scores in that 2-year period (59.3%, 64.8%, and 55.8% predictability, respectively). Final integrated examination scores were significantly correlated (P < 0.001) with National Board of Medical Examiners scores (35% predictability). Multivariate linear regression identified key grades with the greatest predictability of the final integrated examination score at three stages in the program.
Conclusion: This study has demonstrated that it may be possible to identify “at-risk” students relatively early in their studies through continuous data archiving and regular analysis. The data analysis techniques used in this study are not unique to this institution.

Keywords: at-risk students, grade predictability, scores

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