Paper Info
Title
Inferring Visualization Task Properties, User Performance, and User Cognitive Abilities from Eye Gaze Data
Abstract
Information visualization systems have traditionally followed a one-size-fits-all model, typically ignoring an individual user's needs, abilities, and preferences. However, recent research has indicated that visualization performance could be improved by adapting aspects of the visualization to the individual user. To this end, this article presents research aimed at supporting the design of novel user-adaptive visualization systems. In particular, we discuss results on using information on user eye gaze patterns while interacting with a given visualization to predict properties of the user's visualization task; the user's performance (in terms of predicted task completion time); and the user's individual cognitive abilities, such as perceptual speed, visual working memory, and verbal working memory. We provide a detailed analysis of different eye gaze feature sets, as well as over-time accuracies. We show that these predictions are significantly better than a baseline classifier even during the early stages of visualization usage. These findings are then discussed with a view to designing visualization systems that can adapt to the individual user in real time.
Year
DOI
Venue
2014
10.1145/2633043
TIIS
Keywords
DocType
Volume
experimentation,human factors,miscellaneous,adaptive information visualization,eye tracking,adaptation,machine learning
Journal
4
Issue
ISSN
Citations 
2
2160-6455
14
PageRank 
References 
Authors
0.62
29
3
Name
Order
Citations
PageRank
Ben Steichen123916.43
Cristina Conati21970193.27
Giuseppe Carenini31461111.12