RESEARCH LIBRARY
RESEARCH LIBRARY
View the latest publications from members of the NBME research team
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.
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.
Springer International Publishing; 2019
This handbook provides an overview of major developments around diagnostic classification models (DCMs) with regard to modeling, estimation, model checking, scoring, and applications. It brings together not only the current state of the art, but also the theoretical background and models developed for diagnostic classification.
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).
Journal of Graduate Medical Education: June 2018, Vol. 10, No. 3, pp. 337-338
To create examinations with scores that accurately support their intended interpretation and use in a particular setting, examination writers must clearly define what the test is intended to measure (the construct). Writers must also pay careful attention to how content is sampled, how questions are constructed, and how questions perform in their unique testing contexts.1–3 This Rip Out provides guidance for test developers to ensure that scores from MCQ examinations fit their intended purpose.