Paper Info
Title
User-adaptive information visualization: using eye gaze data to infer visualization tasks and user cognitive abilities
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 each individual user. To this end, this paper 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 the user's visualization tasks, as well as user cognitive abilities including perceptual speed, visual working memory, and verbal working memory. We show that such predictions are significantly better than a baseline classifier even during the early stages of visualization usage. These findings are discussed in view of designing visualization systems that can adapt to each individual user in real-time.
Year
DOI
Venue
2013
10.1145/2449396.2449439
IUI
Keywords
Field
DocType
recent research,user-adaptive information visualization,visualization system,visualization task,user eye,user cognitive ability,novel user-adaptive visualization system,verbal working memory,individual user,visualization usage,visualization performance,adaptation,machine learning,eye tracking
Information visualization,Visualization,Computer science,Working memory,Visual analytics,Human–computer interaction,Eye tracking,Classifier (linguistics),Cognition,Multimedia,Perception
Conference
Citations 
PageRank 
References 
54
1.62
23
Authors
3
Name
Order
Citations
PageRank
Ben Steichen123916.43
Giuseppe Carenini21461111.12
Cristina Conati31970193.27