Web-Navigation Skill Assessment Through Eye-Tracking Data

Abstract

Eye-tracking data provide many new options in domain of user modeling. In our work we focus on the automatic detection of web-navigation skill from eye-tracking data. We strive to gain a comprehensive view on the impact of navigation skills on addressing specific user studies and overall interaction on the Web. We proposed an approach for estimating the web navigation skill, with support of self-evaluation questionnaire. We have conducted eye-tracking study with 123 participants. Dataset from this study serves as a base for exploration analysis. We pair different web-navigation behavior metrics with result score from our questionnaire in order to find differences between participant groups. The results of the classification show that some stimuli are more appropriate than others.