๐ค AI Summary
Current communicative visualizations lack effective methods for evaluating their affective objectives, such as attitude change. This work proposes the first systematic evaluation framework specifically designed for affective goals, integrating interdisciplinary assessment tools from education, advocacy, economics, health, and psychology. The framework is rigorously validated through psychometric analysis, qualitative methods, and visualization design experiments. By applying it to real-world scenarios involving complex designs that incorporate personal narratives, the study demonstrates the frameworkโs capacity not only to reliably measure shifts in usersโ affective states but also to exhibit strong theoretical discriminant validity. In doing so, it establishes a clear and actionable bridge between visualization design practices and established multidisciplinary evaluation methodologies.
๐ Abstract
Using learning objectives to define designer intents for communicative visualizations can be a powerful design tool. Cognitive and affective objectives are concrete and specific, which can be translated to assessments when creating, evaluating, or comparing visualization ideas. However, while there are many well-validated assessments for cognitive objectives, affective objectives are uniquely challenging. It is easy to see if a visualization helps someone remember the number of patients in a clinic, but harder to observe the change in their attitudes around donations to a crisis. In this work, we define a set of criteria for selecting assessments--from education, advocacy, economics, health, and psychology--that align with affective objectives. We illustrate the use of the framework in a complex affective design task that combines personal narratives and visualizations. Our chosen assessments allow us to evaluate different designs in the context of our objectives and competing psychological theories.