Inconsistencies in rater-based assessments mainly affect borderline candidates: but using simple heuristics might improve pass-fail decisions

Advances in Health Sciences Education(2024)

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摘要
Research in various areas indicates that expert judgment can be highly inconsistent. However, expert judgment is indispensable in many contexts. In medical education, experts often function as examiners in rater-based assessments. Here, disagreement between examiners can have far-reaching consequences. The literature suggests that inconsistencies in ratings depend on the level of performance a to-be-evaluated candidate shows. This possibility has not been addressed deliberately and with appropriate statistical methods. By adopting the theoretical lens of ecological rationality, we evaluate if easily implementable strategies can enhance decision making in real-world assessment contexts. We address two objectives. First, we investigate the dependence of rater-consistency on performance levels. We recorded videos of mock-exams and had examiners (N=10) evaluate four students’ performances and compare inconsistencies in performance ratings between examiner-pairs using a bootstrapping procedure. Our second objective is to provide an approach that aids decision making by implementing simple heuristics. We found that discrepancies were largely a function of the level of performance the candidates showed. Lower performances were rated more inconsistently than excellent performances. Furthermore, our analyses indicated that the use of simple heuristics might improve decisions in examiner pairs. Inconsistencies in performance judgments continue to be a matter of concern, and we provide empirical evidence for them to be related to candidate performance. We discuss implications for research and the advantages of adopting the perspective of ecological rationality. We point to directions both for further research and for development of assessment practices.
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关键词
Rater-based assessment,Expert judgment,Rater inconsistency,Heuristics,Borderline candidates
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