Towards Automated Web Navigation and Search Skill Assessment: An Eye-tracking Study on the Skill Differences

Abstract

Knowledge about user’s web literacy is important in automated adaptation and personalization of websites. It is also beneficial for website user experience studies. Navigation and search skills are significant constituents of the web literacy. Unfortunately, their automated assessment has so far been hard to achieve. This study investigates possibilities of a user’s skill assessment based on eye-tracking data. We investigate eye-tracking metrics usable for navigation and search skill assessment. We establish a classification of users based on their skills. The paper presents an experimental study with 28 participants. Participants solved various web-search tasks, where the skill differences were manifested. We were able to detect these differences using metrics such as fixation count and time to first fixation on areas of interest.