CHS: Small: Using Learning Objectives for Visualization Design

Regents Of The University Of Michigan - Ann Arbor, Ann Arbor MI

Investigators

Abstract

There are two main types of information visualizations: those used to find insights, and those used to communicate them. While the communicative form is far more common, most research has focused on the insight-finding type. This is problematic for designers of communicative visualization who do not have a great set of tools to test if their design goals are being met. Specifically, it is difficult to answer the question: did the viewer of the visualization learn anything from the visualization or did they simply "read it and forget it?" Without better ways to describe their specific goals and evaluate success, communicative visualization designers often rely on generic heuristic advice about chart types, encodings, narrative techniques, and other design elements to use. This project seeks to create methods and tools for helping designers concretely define their goals in terms of learning objectives, as well as tests and tools to determine if the objectives are met. This, in turn, should improve numerical and graphical literacy as well as enhanced understanding of complex information in domains from news to finance. The research will support growing job categories including visualization designers, computational journalists, and data analysts, as well as organizations focused on public communication. The project activities will also provide research and practical training for undergraduates, graduates, and professionals, while project results will be integrated into accessible educational materials for both visualization-specific classes and as modules for related courses in, e.g., exploratory data analysis, computational journalism, and medical communication. Providing a learning-objective and testing framework for building communicative visualizations requires a deep understanding of how and why designers build their visualizations. Specifically, the goals of this project are (a) developing a learning-objectives "language" for describing communicative intent (e.g., "the viewer will be able to describe the different kinds of trends in the price-rent ratio data"), (b) designing correct and effective testing mechanisms to ensure that these objectives are achieved (e.g., "Based on the price-rent ratio, which of the following cities is displaying a 'bubble' pattern?"), and (c) providing tools -- both workflows and software -- to help designers create learning objectives and tests for readers, as well as for the evaluation of their visualizations. By emphasizing learning objectives for building visualizations, designers will have more confidence that their intended message is communicated by designs that metrics predict will be more successful in communicating that message. Even when no design is optimal across all objectives, the trade-offs will be more salient and easier to understand, allowing designers to make better decisions. Although the focus of this work is on static communicative visualizations for broader public consumption, such as data associated with news stories, the research can be extended to other applications including interactive visualizations, visual analysis systems, explanatory and educational graphics in digital and paper textbooks, and expert-focused forms such as graphics in scientific documents or corporate reports. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.