Showing 1 - 2 of 2 Research Library Publications

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

Leveraging Natural Language Processing: Toward Computer-Assisted Scoring of Patient Notes in the USMLE Step 2 Clinical Skills Exam

Posted: | J. Salt, P. Harik, M. A. Barone

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).

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