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

Abstract

BackgroundThe 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.MethodsAn 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.ResultsWhile 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.ConclusionThis 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.

Performance of first-year health sciences students in a large, diverse, multidisciplinary, first-semester, physiology service module

“…These studies deal, among others, with factors such as attendance (3,9), predicting the reliability of admission criteria, such as admission tests, interviews, high school subjects, scores, etc. (2,5,6,12,19,24), and the variety of ways that students enrolled in a medical school can be profiled (10,17,30,31,32). The pertinent points made by these authors are discussed further below as they pertain to our findings.…”

“…There is also a large body of publications dealing with the selection/ admission of students into the health sciences (2,5,6). By comparison, there seems to be a relative dearth of papers in the recent literature on the identification of health science students (24) who are struggling with their studies particularly in our specific context, namely, large, diverse, and multidisciplinary modules. Most of these studies came from one institution and deal with the identification of medical students only (10,31).…”

Factors related to the achievement of the National License Examination Part 1 of medical students in Faculty of Medicine Vajira Hospital, Navamindradhiraj University

“…These seven factors were: personal factor (gender), academic background (GPA, aptitude, and admission scores), and intrinsic factors (learning and examination behavior as well as attitude toward the NLE). Although high school grade was reported by some as a useful predictor of medical study (Abdulrazzaq & Ibrahim, 1993;Al-Nasir & Robertson, 2001;Haidinger et al, 2006;Lumb & Vail, 2004) especially premedical school grade in science * Numbers of students who responded to the 2 sections of preparation for examination and attitude towards the examination were 203 for the result of passed vs failed and was 175 for the result of level of scores among passing students (Al-Nasir & Robertson, 2001;James & Chilvers, 2001), Shaban & Mc Lean, (2011) could not demonstrate that high school grade was important We did not include high school grade as a potential factor in our study because they were not standardized throughout the country but rather depended on the liberality and policy of each school.…”

Predicting academic performance: a systematic literature review

“…Only through understanding both what works and what does not can we form a holistic understanding of the topic. [7, 22, 46, 47, 62, 63, 71-73, 83, 87, 89, 94, 104, 107, 147, 149, 166, 172, 173, 177, 184, 186, 206, 208, 215, 218, 220, 231, 232, 244, 279, 296, 299, 315, 319, 327, 329, 337, 341, 358, 359, 373, 374, 407, 411, 424] Exam / Post-test Grade or Score [9,10,21,24,33,35,52,59,67,68,77,80,81,85,90,96,109,114,116,127,136,152,162,163,169,193,195,199,202,205,214,217,224,233,238,241,270,274,275,277,284,287,301,302,309,314,…”