Constructing user models from eye gaze data in the domain of information visualization

A user-adaptive information visualization system capable of learning models of users and the visualization tasks they perform could provide interventions optimized for helping specific users in specific task contexts. This thesis investigates the accuracy of predicting visualization tasks, user performance on tasks, and user traits from gaze data. It is shown that predictions made with a logistic regression model are significantly better than a baseline classifier, with particularly strong results for predicting task type and user performance. Furthermore, classifiers built with interface-independent are compared with classifiers built with interface-dependent features. Interface-independent features are shown to be comparable or superior to interface-dependent ones. Adding highlighting interventions to trials is shown to have an effect on the accuracy of predictive models trained on the data from those trials and these effects are discussed. The applicability of all results to real-time classification is tested using datasets that limit the amount of observations that are processed into classification features. Finally, trends in features selected by classifiers and classifier accuracies over time are explored as a means to interpret the performance of the tested classification models.Science, Faculty ofComputer Science, Department ofGraduat