Toward user-adaptive visualizations : further results on real-time prediction of user cognitive abilities from action and eye-tracking data

Previous work has shown that some user cognitive abilities relevant for processing information visualizations can be predicted from eye tracking data. Performing this type of user modeling is important for devising user-adaptive visualizations that can adapt to a user’s abilities as needed during the interaction. In this thesis, we contribute to previous work by extending the type of visualizations considered and the set of cognitive abilities that can be predicted from gaze data, thus providing evidence on the generality of these findings. We also evaluate how quality of gaze data impacts prediction. Finally, we further extend previous work by investigating interaction data as an alternative source to predict our target user characteristics. We present a formal comparison of predictors based solely on gaze data, on interaction data, or on a combination of the two.Science, Faculty ofComputer Science, Department ofGraduat