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.

Journal of Educational Measurement: Volume 55, Issue 2, Pages 308-327

The widespread move to computerized test delivery has led to the development of new approaches to evaluating how examinees use testing time and to new metrics designed to provide evidence about the extent to which time limits impact performance. Much of the existing research is based on these types of observational metrics; relatively few studies use randomized experiments to evaluate the impact time limits on scores. Of those studies that do report on randomized experiments, none directly compare the experimental results to evidence from observational metrics to evaluate the extent to which these metrics are able to sensitively identify conditions in which time constraints actually impact scores. The present study provides such evidence based on data from a medical licensing examination.