Learnability of Interactive Coordinated-View Visualizations Sujatha Krishnamoorthy, Christopher North Department of Computer Science, Virginia Tech, Blacksburg, VA 24060 USA {sukrish2@vt.edu, north@cs.vt.edu} Abstract This paper examines the Human Computer Interaction issue of learnability of interactive coordinated-view visualizations. We take the case of DataMaps, a Census data visualization tool intended for a general audience with a huge percentage of novices. Usability tests conducted on DataMaps revealed three main kinds of problems that novices faced: they could not make strategic selections of coordinated visualizations according to a given task, they lacked familiarity with the nature of the attributes, and there were several misunderstandings of visual syntax and interaction widget usage. We outline design features which are desirable for novice-friendliness: Task based organization of coordinated views to enable strategic selection of views to suit the task, Data centric approach to familiarize novices with data, Self disclosure of visual syntax features and interaction mechanisms by the interface. The design should be such that they can smoothly transition from being a novice to expert. We examine how these principles may be applied to DataMaps to re-design it for “novice-friendliness”. 1. Introduction The learnability of interactive, coordinated-view visualizations is an important issue in several scenarios. Consider a visualization interface in a museum or a website, available to a varied audience. Several users might be accessing the visualization tool only for a one- time exploratory session. A visitor in a museum might want to interact with the display for a while. Novice- friendliness is crucial in such cases and we are justified in researching learnability issues in visualizations. We define a novice user as someone who does not have experience with using interactive multiple-view visualizations. However, we do need to assume a “lowest common denominator profile” for novice users to design the interface with respect to that profile. We assume that a user, although novice, necessarily possesses: • General familiarity with the notion of using visual representations of information (as opposed to textual paragraphs). For example, they would be familiar with visual representations encountered in day-to-day life, such as pie charts and bar graphs and weather information on maps as seen in weather news broadcasts. • General familiarity with W-I-M-P interfaces (Windows Icons Menus Pointer interface) where the user must rely on icons, buttons, and dialog boxes for executing operations. We focused our studies on a particular visualization tool called DataMaps, supported by US Census Bureau. We expect that a typical user browsing the Census website would be familiar with WIMP interfaces. An informal survey revealed that bar-charts and pie-charts are the most commonly encountered and easily understood visualizations for those who have no prior exposure to interactive information visualizations. It also showed us that it is acceptable to assume basic familiarity with the notion of using visual representations in novices. In sections 2, 3 and 4 of this document, we provide a description of DataMaps, the usability test conducted on it and the qualitative results obtained, respectively. In sections 5 we theorize about learnability issues and in section 6, we outline design principles which enhance learnability. In section 7, we show how the design principles may be applied to re-design DataMaps for novice friendliness. 2. Background on DataMaps DataMaps [2] is a front-end tool for visualization and analysis of census data on the United States. Data has been collected for approximately 8000 attributes. “Attributes” are items such as “total population” “percentage white population” “percentage black population” and so on. These attributes are grouped together by categories such as “Age”, “Agriculture”, “Banking”,” Crime” etc. See figure 1.