RESEARCH LIBRARY

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.

Diagnosis: Volume 10, Issue 1, Pages 54-60

This op-ed discusses the advantages of leveraging natural language processing (NLP) in the assessment of clinical reasoning. It also provides an overview of INCITE, the Intelligent Clinical Text Evaluator, a scalable NLP-based computer-assisted scoring system that was developed to measure clinical reasoning ability as assessed in the written documentation portion of the now-discontinued USMLE Step 2 Clinical Skills examination. 

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.

Applied Psychological Measurement: Volume 47, issue 1, page(s) 34-47

This study used simulation to investigate the performance of the t-test method in detecting outliers and compared its performance with other outlier detection methods, including the logit difference method with 0.5 and 0.3 as the cutoff values and the robust z statistic with 2.7 as the cutoff value.

Journal of Educational Measurement: Volume 59, Issue 2, Pages 140-160

A conceptual framework for thinking about the problem of score comparability is given followed by a description of three classes of connectives. Examples from the history of innovations in testing are given for each class.

JMIR Medical Education: Volume 8 - Issue 2 - e30988

This article aims to compare the reliability of two assessment groups (crowdsourced laypeople and patient advocates) in rating physician error disclosure communication skills using the Video-Based Communication Assessment app.

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.

Journal of Educational Measurement: Volume 58, Issue 4, Pages 515-537

In this paper, the NBME team reports the results an eye-tracking study designed to evaluate how the presence of the options in multiple-choice questions impacts the way medical students responded to questions designed to evaluate clinical reasoning. Examples of the types of data that can be extracted are presented. We then discuss the implications of these results for evaluating the validity of inferences made based on the type of items used in this study.

Proceedings of the 28th International Conference on Computational Linguistics

This paper brings together approaches from the fields of NLP and psychometric measurement to address the problem of predicting examinee proficiency from responses to short-answer questions (SAQs).