RESEARCH LIBRARY
RESEARCH LIBRARY
View the latest publications from members of the NBME research team
Academic Medicine: Volume 99 - Issue 7 - Pages 778-783
This study examined score comparability between in-person and remote proctored administrations of the 2020 Internal Medicine In-Training Examination (IM-ITE) during the COVID-19 pandemic. Analysis of data from 27,115 IM residents revealed statistically significant but educationally nonsignificant differences in predicted scores, with slightly larger variations observed for first-year residents. Overall, performance did not substantially differ between the two testing modalities, supporting the continued use of remote proctoring for the IM-ITE amidst pandemic-related disruptions.
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.
Medical Teacher: Volume 45 - Issue 6, Pages 565-573
This guide aims aim to describe practical considerations involved in reading and conducting studies in medical education using Artificial Intelligence (AI), define basic terminology and identify which medical education problems and data are ideally-suited for using AI.
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.
Advances in Health Sciences Education: Volume 25, p 1057–1086 (2020)
This critical review explores: (1) published applications of data science and ML in HPE literature and (2) the potential role of data science and ML in shifting theoretical and epistemological perspectives in HPE research and practice.