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RESEARCH LIBRARY

View the latest publications from members of the NBME research team

Showing 1 - 10 of 28 Research Library Publications
Posted: | John Norcini, Irina Grabovsky, Michael A. Barone, M. Brownell Anderson, Ravi S. Pandian, Alex J. Mechaber

Academic Medicine: Volume 99 - Issue 3 - p 325-330

 

This retrospective cohort study investigates the association between United States Medical Licensing Examination (USMLE) scores and outcomes in 196,881 hospitalizations in Pennsylvania over 3 years.

Posted: | Victoria Yaneva, Peter Baldwin, Daniel P. Jurich, Kimberly Swygert, Brian E. Clauser

Academic Medicine: Volume 99 - Issue 2 - p 192-197

 

This report investigates the potential of artificial intelligence (AI) agents, exemplified by ChatGPT, to perform on the United States Medical Licensing Examination (USMLE), following reports of its successful performance on sample items. 

Posted: | Victoria Yaneva (editor), Matthias von Davier (editor)

Advancing Natural Language Processing in Educational Assessment

 

This book examines the use of natural language technology in educational testing, measurement, and assessment. Recent developments in natural language processing (NLP) have enabled large-scale educational applications, though scholars and professionals may lack a shared understanding of the strengths and limitations of NLP in assessment as well as the challenges that testing organizations face in implementation. This first-of-its-kind book provides evidence-based practices for the use of NLP-based approaches to automated text and speech scoring, language proficiency assessment, technology-assisted item generation, gamification, learner feedback, and beyond.

Posted: | Victoria Yaneva, Peter Baldwin, Le An Ha, Christopher Runyon

Advancing Natural Language Processing in Educational Assessment: Pages 167-182

 

This chapter discusses the evolution of natural language processing (NLP) approaches to text representation and how different ways of representing text can be utilized for a relatively understudied task in educational assessment – that of predicting item characteristics from item text.

Posted: | Polina Harik, Janet Mee, Christopher Runyon, Brian E. Clauser

Advancing Natural Language Processing in Educational Assessment: Pages 58-73

 

This chapter describes INCITE, an NLP-based system for scoring free-text responses. It emphasizes the importance of context and the system’s intended use and explains how each component of the system contributed to its accuracy.

Posted: | Hanin Rashid, Christopher Runyon, Jesse Burk-Rafel, Monica M. Cuddy, Liselotte Dyrbye, Katie Arnhart, Ulana Luciw-Dubas, Hilit F. Mechaber, Steve Lieberman, Miguel Paniagua

Academic Medicine: Volume 97 - Issue 11S - Page S176

 

As Step 1 begins to transition to pass/fail, it is interesting to consider the impact of score goal on wellness. This study examines the relationship between goal score, gender, and students’ self-reported anxiety, stress, and overall distress immediately following their completion of Step 1.

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.

Posted: | Victoria Yaneva, Janet Mee, Le Ha, Polina Harik, Michael Jodoin, Alex Mechaber

Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies - p 2880–2886

 

This paper presents a corpus of 43,985 clinical patient notes (PNs) written by 35,156 examinees during the high-stakes USMLE® Step 2 Clinical Skills examination.

Posted: | Monica M. Cuddy, Chunyan Liu, Wenli Ouyang, Michael A. Barone, Aaron Young, David A. Johnson

Academic Medicine: June 2022

 

This study examines the associations between Step 3 scores and subsequent receipt of disciplinary action taken by state medical boards for problematic behavior in practice. It analyzes Step 3 total, Step 3 computer-based case simulation (CCS), and Step 3multiple-choice question (MCQ) scores.

Posted: | Daniel Jurich, Chunyan Liu, Amanda Clauser

Journal of Graduate Medical Education: Volume 14, Issue 3, Pages 353-354

 

Letter to the editor.