RESEARCH LIBRARY

Educational Assessment

This study proposes four indices to quantify item influence and distinguishes them from other available item and test measures. We use simulation methods to evaluate and provide guidelines for interpreting each index, followed by a real data application to illustrate their use in practice. We discuss theoretical considerations regarding when influence presents a psychometric concern and other practical concerns such as how the indices function when reducing influence imbalance.

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.

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.

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.

Integrating Timing Considerations to Improve Testing Practices

This chapter addresses a different aspect of the use of timing data: it provides a framework for understanding how an examinee's use of time interfaces with time limits to impact both test performance and the validity of inferences made based on test scores. It focuses primarily on examinations that are administered as part of the physician licensure process.

IEEE Transactions on Neural Systems and Rehabilitation Engineering

The purpose of this study is to test whether visual processing differences between adults with and without high-functioning autism captured through eye tracking can be used to detect autism.

Handbook of Automated Scoring

In this chapter we describe the historical background that led to development of the simulations and the subsequent refinement of the construct that occurred as the interface was being developed. We then describe the evolution of the automated scoring procedures from linear regression modeling to rule-based procedures.