ArticleBMC medical education2022
Validation of response processes in medical assessment using an explanatory item response model.
Article in BMC medical education, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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3 authors at 1 institution in 1 country.
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Abstract
backgroundResponse process validation is a crucial source of test validity. The expected cognitive load scale was created based on the reflection of the mental effort by which borderline students solve an item defined by experts. The stem length affects the students' extraneous cognitive load. The purposes of this study were to develop an exam for medical students and corroborate the response process validity by analyzing the correlation between the expected cognitive load, stem length, and the difficulty.
methodsThis was a correlational study. Five medical teachers as the experts and 183 third-year medical students were enrolled from the Faculty of Medicine, Prince of Songkla University, Thailand. The instruments used were a medical physiology exam and a three-level expected cognitive load evaluation form judged by medical teachers. Data were analyzed using an explanatory item response model.
resultsThe test consists of 20 items and 21 possible scores. The median score was 8, with a quartile deviation of 1.5. Nine items had long stems (more than two lines). Sixteen items were judged as high (level 2 or 3) expected cognitive load. When adding the expected cognitive load in a Rasch model, the expected cognitive load significantly correlated with item difficulty. In the Rasch model that included both the expected cognitive load and stem length, a long stem had a greater effect on item difficulty than low expected cognitive load. However, the Rasch model showed the best fit.
conclusionsThe long stem had a stronger correlation with test difficulty than expected cognitive load, which indirectly implied response process validity. We suggest incorporating stem length and expected cognitive load to create an appropriate distribution of the difficulty of the entire test.
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