UNDERSTANDING EXPERT AND NOVICE MEANING-MAKING FROM GLOBAL DATA VISUALIZATIONS USING INTERVIEWS AND EYE-TRACKING

Scientists often create visualizations with cultural conventions such that novices, who lack the extensive training of professionals, cannot make meaning from them in the same way as experts. This research addresses the question of how scientists and novices analyze global data visualizations and how they use scaffolding, that is, supporting details or labels added to the images to clarify the meaning of the data. The project uses multiple methodologies from education and neuroscience to address questions of how people make meaning.

Previous work shows changing culturally “scientific” color scales and measurement units to more broadly culturally-relevant colors and units improved comprehension of the overall scientific meaning for both teachers and science center visitors. Adding geographic labels, borders, and legends based on the ways users naturally read or scan pages make the areas represented in an image more immediately recognizable. Perceptually, color scales built to work with, rather than against the human visual system also could facilitate meaning-making.

Clinical interviews with subjects elicited areas of confusion and differences in meaning-making between experts (n=12) and novices (n=18) that subjects can articulate. Eye-tracking studies show differences in non-conscious attention to features in visualizations. Results from both methods with unchanged and scaffolded global ocean data images, plus suggestions for further improving the images will be presented. Experts indicate much of the knowledge they apply to understanding the images comes from graduate and professional work, which novices lack. In addition, pilot eye-tracking data indicates experts more readily make use of the title and color bar, as well as view more of the image than novices.

Our goal is to understand differences in meaning-making in order to create images more meaningful to everyone and to foster better communication between scientists and their publics. Especially in out-of-school settings, the less time and energy users expend comprehending the basic features of what is depicted and where it is, the more effort they can spend recognizing and evaluating patterns in the data.