RESEARCH LIBRARY

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.

Advances in Health Sciences Education: Volume 27, p 1401–1422

After collecting eye-tracking data from 26 students responding to clinical MCQs, analysis is performed by providing 119 eye-tracking features as input for a machine-learning model aiming to classify correct and incorrect responses. The predictive power of various combinations of features within the model is evaluated to understand how different feature interactions contribute to the predictions.

ACM SIGACCESS Accessibility and Computing

In this article, we first summarise STA (Scanpath Trend Analysis) with its application in autism detection, and then discuss future directions for this research.

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.

Western Journal of Emergency Medicine: Integrating Emergency Care with Population Health, 19(1)

This review is a descriptive summary of the development of National EM M4 examinations, Version 1 (V1) and Version 2 (V2), and the NBME EM Advanced Clinical Examination (ACE) and their relevant usage and performance data. In particular, it describes how examination content was edited to affect desired changes in examination performance data and offers a model for educators seeking to develop their own examinations.