RESEARCH LIBRARY

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.

Academic Medicine: Volume 88 - Issue 11 - p 1670-1675

From 2007 through 2012, the NBME team reviewed literature in physician–patient communication, examined performance characteristics of the Step 2 CS exam, observed case development and quality assurance processes, interviewed SPs and their trainers, and reviewed video recordings of examinee–SP interactions.  The authors describe perspectives gained by their team from the review process and outline the resulting enhancements to the Step 2 CS exam, some of which were rolled out in June 2012.