PROJECT TITLE :

VA2: A Visual Analytics Approach for // Evaluating Visual Analytics Applications

ABSTRACT:

Evaluation has become a elementary part of visualization analysis and researchers have utilized several approaches from the sector of human-computer interaction like measures of task performance, thinking aloud protocols, and analysis of interaction logs. Recently, eye tracking has also become standard to research visual strategies of users in this context. This has added another modality and more data, that requires special visualization techniques to research this information. But, only few approaches exist that aim at an integrated analysis of multiple concurrent evaluation procedures. The selection, complexity, and sheer amount of such coupled multi-supply knowledge streams need a visible analytics approach. Our approach provides a highly interactive visualization setting to show and analyze thinking aloud, interaction, and eye movement knowledge in shut relation. Automatic pattern finding algorithms enable an efficient exploratory search and support the reasoning process to derive common eye-interaction-thinking patterns between participants. Also, our tool equips researchers with mechanisms for looking out and verifying expected usage patterns. We tend to apply our approach to a user study involving a visible analytics application and we have a tendency to discuss insights gained from this joint analysis. We tend to anticipate our approach to be applicable to other combos of evaluation techniques and a broad category of visualization applications.