Resitting a high-stakes postgraduate medical examination on multiple occasions: nonlinear multilevel modelling of performance in the MRCP(UK) examinations

BMC Medicine

Research Article

Katarzyna Ludka¹  IC McManus² 

[1] Academic Centre for Medical Education, Division of Medical Education, University College London, Gower Street, WC1E 6BT, London, UK;Academic Centre for Medical Education, Division of Medical Education, University College London, Gower Street, WC1E 6BT, London, UK;Division of Psychology and Language Sciences, University College London, Gower Street, WC1E 6BT, London, UK;

关键词: General Medical Council;    Pass Mark;    Individual Candidate;    True Ability;    Negative Exponential Model;   

DOI  :  10.1186/1741-7015-10-60

 received in 2011-11-30, accepted in 2012-06-14,  发布年份 2012

来源: Springer

【 摘 要 】

BackgroundFailure rates in postgraduate examinations are often high and many candidates therefore retake examinations on several or even many times. Little, however, is known about how candidates perform across those multiple attempts. A key theoretical question to be resolved is whether candidates pass at a resit because they have got better, having acquired more knowledge or skills, or whether they have got lucky, chance helping them to get over the pass mark. In the UK, the issue of resits has become of particular interest since the General Medical Council issued a consultation and is considering limiting the number of attempts candidates may make at examinations.MethodsSince 1999 the examination for Membership of the Royal Colleges of Physicians of the United Kingdom (MRCP(UK)) has imposed no limit on the number of attempts candidates can make at its Part 1, Part2 or PACES (Clinical) examination. The present study examined the performance of candidates on the examinations from 2002/2003 to 2010, during which time the examination structure has been stable. Data were available for 70,856 attempts at Part 1 by 39,335 candidates, 37,654 attempts at Part 2 by 23,637 candidates and 40,303 attempts at PACES by 21,270 candidates, with the maximum number of attempts being 26, 21 and 14, respectively. The results were analyzed using multilevel modelling, fitting negative exponential growth curves to individual candidate performance.ResultsThe number of candidates taking the assessment falls exponentially at each attempt. Performance improves across attempts, with evidence in the Part 1 examination that candidates are still improving up to the tenth attempt, with a similar improvement up to the fourth attempt in Part 2 and the sixth attempt at PACES. Random effects modelling shows that candidates begin at a starting level, with performance increasing by a smaller amount at each attempt, with evidence of a maximum, asymptotic level for candidates, and candidates showing variation in starting level, rate of improvement and maximum level. Modelling longitudinal performance across the three diets (sittings) shows that the starting level at Part 1 predicts starting level at both Part 2 and PACES, and the rate of improvement at Part 1 also predicts the starting level at Part 2 and PACES.ConclusionCandidates continue to show evidence of true improvement in performance up to at least the tenth attempt at MRCP(UK) Part 1, although there are individual differences in the starting level, the rate of improvement and the maximum level that can be achieved. Such findings provide little support for arguments that candidates should only be allowed a fixed number of attempts at an examination. However, unlimited numbers of attempts are also difficult to justify because of the inevitable and ever increasing role that luck must play with increasing numbers of resits, so that the issue of multiple attempts might be better addressed by tackling the difficult question of how a pass mark should increase with each attempt at an exam.

【 授权许可】

CC BY   
© McManus and Ludka; licensee BioMed Central Ltd. 2012