RESEARCH LIBRARY

Academic Medicine: July 2019 - Volume 94 - Issue 7 - p 926-927

A response to concerns regarding potential bias in the implementation of machine learning (ML) to scoring of the United States Medical Licensing Examination Step 2 Clinical Skills (CS) patient notes (PN).

Academic Medicine: March 2019 - Volume 94 - Issue 3 - p 371-377

Schools undergoing curricular reform are reconsidering the optimal timing of Step 1. This study provides a psychometric investigation of the impact on United States Medical Licensing Examination Step 1 scores of changing the timing of Step 1 from after completion of the basic science curricula to after core clerkships.

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).

Investigación en Educación Médica, Vol. 8, Núm. 29, 2019

Journal of Educational Measurement: Volume 55, Issue 4, Pages 582-594

This article proposes and evaluates a new method that implements computerized adaptive testing (CAT) without any restriction on item review. In particular, it evaluates the new method in terms of the accuracy on ability estimates and the robustness against test‐manipulation strategies. This study shows that the newly proposed method is promising in a win‐win situation: examinees have full freedom to review and change answers, and the impacts of test‐manipulation strategies are undermined.

Medical Teacher: Volume 40 - Issue 8 - p 838-841

Adaptive learning requires frequent and valid assessments for learners to track progress against their goals. This study determined if multiple-choice questions (MCQs) “crowdsourced” from medical learners could meet the standards of many large-scale testing programs.

Journal of Medical Regulation (2018) 104 (2): 51–57

There have been a number of important stakeholder opinions critical of the Step 2 Clinical Skills Examination (CS) in the United States Medical Licensing Examination (USMLE) licensure sequence. The Resident Program Director (RPD) Awareness survey was convened to gauge perceptions of current and potential Step 2 CS use, attitudes towards the importance of residents' clinical skills, and awareness of a medical student petition against Step 2 CS. This was a cross-sectional survey which resulted in 205 responses from a representative sampling of RPDs across various specialties, regions and program sizes.

Journal of Educational Measurement: Volume 55, Issue 2, Pages 308-327

The widespread move to computerized test delivery has led to the development of new approaches to evaluating how examinees use testing time and to new metrics designed to provide evidence about the extent to which time limits impact performance. Much of the existing research is based on these types of observational metrics; relatively few studies use randomized experiments to evaluate the impact time limits on scores. Of those studies that do report on randomized experiments, none directly compare the experimental results to evidence from observational metrics to evaluate the extent to which these metrics are able to sensitively identify conditions in which time constraints actually impact scores. The present study provides such evidence based on data from a medical licensing examination.

Academic Medicine: May 2018 - Volume 93 - Issue 5 - p 781-785

In 2007, the United States Medical Licensing Examination embedded multimedia simulations of heart sounds into multiple-choice questions. This study investigated changes in item difficulty as determined by examinee performance over time. The data reflect outcomes obtained following initial use of multimedia items from 2007 through 2012, after which an interface change occurred.

Quality Assurance in Education, Vol. 26 No. 2, pp. 150-152

An introduction to a special issue of Quality Assurance in Education featuring papers based on presentations at a two-day international seminar on managing the quality of data collection in large-scale assessments.