Quantitative Analysis of Search Sessions Enhanced by Gaze Tracking with Dynamic Areas of Interest

摘要

After presenting the ezDL experimental framework for the evaluation of user interfaces to digital library systems, we describe a new method for the quantitative analysis of user search sessions at a cognitive level. We combine system logs with gaze tracking, which is enhanced by a new framework for capturing dynamic areas of interest. This observation data is mapped onto a user action level. Then the user search process is modeled as a Markov-chain. The analysis not only allows for a better understanding of user behavior, but also points out possible system improvements.