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User characteristics and eye tracking to inform the design of user-adaptive information visualizations

University of British Columbia

Amidst an ever-increasing amount of digital information, information visualizations have become a fundamental tool to support tasks for discovering, presenting, and understanding the many underlying trends in this data. Ongoing effort to improve the effectiveness of visualizations however has been typically limited to their design and evaluation following a one size-fits-all model, meaning that they do not take into account the individual differences of their users. There is mounting evidence though, that user differences such as cognitive abilities, personality traits, learning abilities, and preferences can significantly influence user performance and satisfaction during information visualization tasks, thus motivating a need for personalization. In this thesis, our primary goal is to inform the design of user-adaptive visualizations, namely, visualizations that aim to recognize and adapt to each user’s specific needs. We conducted three different user studies to address several key questions for designing user-adaptive visualizations: i) What characteristics of the user should be considered to drive adaptation? ii) How can a visualization system adequately adapt to these user characteristics? and iii) When should adaptations be delivered in order to maximize effectiveness and reduce intrusiveness? In our first study, we tested the effectiveness of highlighting interventions on bar chart visualizations and examined the role that several cognitive abilities may have on visualization processing. Results from this study provide contributions showing that: highlighting relevant information in real-time can be beneficial to bar chart processing; certain user characteristics may only warrant adaptation as task complexity increases; users with low Verbal Working Memory may need interventions that facilitate processing of the visualization’s legend; and adapting to users’ level of Evolving Skill with a visualization is possible using eye tracking to make real-time predictions of this user characteristic. In our second and third study, we investigate visualizations embedded in narrative text, referred to as Magazine Style Narrative Visualization (MSNV). Results from these two studies provide contributions showing that: Verbal Working Memory and English Reading Ability can impact users’ ability to effectively process MSNVs supporting a need for adaptation; and in particular low Reading Ability users might benefit from adaptations helping them locate relevant information in the visualizations.

Text

2019-09-11

Attribution-NonCommercial-NoDerivatives 4.0 International

http://hdl.handle.net/2429/71661

Computer Science

University of British Columbia

UBCV

http://creativecommons.org/licenses/by-nc-nd/4.0/