Our Ph.D. student, Jonathan Pulliza, has successfully defended his dissertation titled titled “Let the Robot Do It For Me: Assessing Voice As a Modality for Visual Analytics for Novice Users”. The committee included Chirag Shah (University of Washington, Chair), Nina Wacholder (Rutgers University), Mark Aakhus (Rutgers University), and Melanie Tory (Tableau).
Pulliza’s study focuses on understanding how the voice system facilitates novice users in Visual Analytics (VA). He found that participants chose to use the voice system because of its convenience, ability to get a quick start on their work, and better access to some functions that they could not find in the traditional screen interface. Participants refrained from choosing voice because of their previous experiences. They felt that using the voice system would not provide then all access to the more complicated VA system. They then often chose to struggle with the visual interface instead of using the voice system for assistance.
The growth of Visual Analytics (VA) systems has been driven by the need to explore and understand large datasets across many domains. Applications such as Tableau were developed with the goal of better supporting novice users to generate data visualizations and complete their tasks. However, novice users still face many challenges in using VA systems, especially in complex tasks outside of simple trend identification, such as exploratory tasks. Many of the issues stem from the novice users’ inability to reconcile their questions or representations of the data with the visualizations presented using the interactions provided by the system.
With the improvement in natural language processing technology and the increased prevalence of voice interfaces, there is a renewed interest in developing voice interactions for VA systems. The goal is to enable users to ask questions directly to the system or to indicate specific actions using natural language, which may better facilitate access to functions available in the VA system. Previous approaches have tended to build systems in a screen-only environment in order to encourage interaction through voice. Though they did produce significant results and guidance for the technical challenges of voice in VA, it is important to understand how the use of a voice system would affect novice users within their most common context instead of moving them into new environments. It is also important to understand when a novice user would choose to use a voice modality when the traditional keyboard and mouse modality is also available.
This study is an attempt to understand the circumstances under which novice users of a VA system would choose to interact with using their voice in a traditional desktop environment, and whether the voice system better facilitates access to available functionalities. Given the users choose the voice system, do they choose different functions than those with only a keyboard and a mouse? Using a Wizard of Oz set up in the place of an automated voice system, we find that the participants chose to use the voice system because of its convenience, ability to get a quick start on their work, and in some situations where they could not find a specific function in the interface. Overall function choices were not found to be significantly different between those who had access to the voice system versus those who did not, though there were a few cases where participants were able to access less common functions compared to a control group. Participants refrained from choosing voice because their previous experiences with voice systems had led them to believe all voice systems were not capable of addressing their task needs. They also felt using the voice system was incongruent with gaining mastery of the underlying VA system, as the convenience of using the voice system could lead to its use as a crutch. Participants then often chose to struggle with the visual interface instead of using the voice system for assistance. In this way, they prioritized building a better mental model of the system over building a better sense of the data set and accomplishing the task.