Showing 1 - 4 of 4 Research Library Publications

Revisiting Retake Policy: Analyzing the Success Rates of Examinees With Multiple Attempts on the United States Medical Licensing Examination

Posted: | Jonathan D. Rubright, Thai Q. Ong, Michael G. Jodoin, David A. Johnson, Michael A. Barone

Academic Medicine: Volume 97 - Issue 8 - Pages 1219-1225

 

Since 2012, the United States Medical Licensing Examination (USMLE) has maintained a policy of ≤ 6 attempts on any examination component. The purpose of this study was to empirically examine the appropriateness of existing USMLE retake policy.

Category:Product-Oriented Research, USMLE, Assessment-Oriented Research, Links to Outcomes

In Reply to D'Eon and Kleinheksel

Posted: | Katie L. Arnhart, Monica M. Cuddy, David Johnson, Michael A. Barone, Aaron Young

Academic Medicine: Volume 97 - Issue 4 - Pages 476-477

 

Response to to emphasize that although findings support a relationship between multiple USMLE attempts and increased likelihood of receiving disciplinary actions, the findings in isolation are not sufficient for proposing new policy on how many attempts should be allowed.

Category:Product-Oriented Research, USMLE, Assessment-Oriented Research, Reliability/Validity, Links to Outcomes

Medical Student Experiences With Electronic Health Records Nationally: A Longitudinal Analysis Including School-Level Effects

Posted: | Monica M. Cuddy, Lauren M. Foster, Paul M. Wallach, Maya M. Hammoud, David B. Swanson

Academic Medicine: Volume 97 - Issue 2 - Pages 262-270

 

This study examined shifts in U.S. medical student interactions with EHRs during their clinical education, 2012–2016, and how these interactions varied by clerkship within and across medical schools.

Category:Health Professions

Leveraging Machine Learning Technology to Improve Accuracy and Efficiency of Identification of Enemy Item Pairs

Posted: | Ian Micir, Kimberly Swygert, Jean D'Angelo

Journal of Applied Technology: Volume 23 - Special Issue 1 - Pages 30-40

 

The interpretations of test scores in secure, high-stakes environments are dependent on several assumptions, one of which is that examinee responses to items are independent and no enemy items are included on the same forms. This paper documents the development and implementation of a C#-based application that uses Natural Language Processing (NLP) and Machine Learning (ML) techniques to produce prioritized predictions of item enemy statuses within a large item bank.

Category:Assessment-Oriented Research, Scoring, Applications of Technology