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.

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.

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.

Neural Engineering Techniques for Autism Spectrum Disorder: Volume 2, Pages 63-79

Automated detection of high-functioning autism in adults is a highly challenging and understudied problem. In search of a way to automatically detect the condition, this chapter explores how eye-tracking data from reading tasks can be used.

Behavior & Information Technology

This study builds upon prior work in this area that focused on developing a machine-learning classifier trained on gaze data from web-related tasks to detect ASD in adults. Using the same data, we show that a new data pre-processing approach, combined with an exploration of the performance of different classification algorithms, leads to an increased classification accuracy compared to prior work.

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.

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.

Academic Medicine: Volume 97 - Issue 2 - Pages 262-270

This study examined shifts in U.S. medical student interactions with EHRs during their clinical education, 2012–2016, and how these interactions varied by clerkship within and across medical schools.

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.