RESEARCH LIBRARY
RESEARCH LIBRARY
View the latest publications from members of the NBME research team
Journal of Educational Measurement: Volume 58, Issue 4, Pages 515-537
In this paper, the NBME team reports the results an eye-tracking study designed to evaluate how the presence of the options in multiple-choice questions impacts the way medical students responded to questions designed to evaluate clinical reasoning. Examples of the types of data that can be extracted are presented. We then discuss the implications of these results for evaluating the validity of inferences made based on the type of items used in this study.
Proceedings of the 28th International Conference on Computational Linguistics
This paper brings together approaches from the fields of NLP and psychometric measurement to address the problem of predicting examinee proficiency from responses to short-answer questions (SAQs).
Educational Measurement: Issues and Practice
This article aims to answer the question: when the assumption that examinees may apply themselves fully yet still respond incorrectly is violated, what are the consequences of using the modified model proposed by Lewis and his colleagues?
Journal of Educational Measurement: Volume 57, Issue 2, Pages 216-229
This article presents two generalizability-theory–based analyses of the proportion of the item variance that contributes to error in the cut score. For one approach, variance components are estimated on the probability (or proportion-correct) scale of the Angoff judgments, and for the other, the judgments are transferred to the theta scale of an item response theory model before estimating the variance components.
Academic Medicine: March 2019 - Volume 94 - Issue 3 - p 314-316
The United States Medical Licensing Examination Step 2 Clinical Skills (CS) exam uses physician raters to evaluate patient notes written by examinees. In this Invited Commentary, the authors describe the ways in which the Step 2 CS exam could benefit from adopting a computer-assisted scoring approach that combines physician raters’ judgments with computer-generated scores based on natural language processing (NLP).