  Despite the ubiquity of communicative visualizations, specifying
communicative intent during design is ad hoc. Whether we are selecting from a
set of visualizations, commissioning someone to produce them, or creating them
ourselves, an effective way of specifying intent can help guide this process.
Ideally, we would have a concise and shared specification language. In previous
work, we have argued that communicative intents can be viewed as a
learning/assessment problem (i.e., what should the reader learn and what test
should they do well on). Learning-based specification formats are linked (e.g.,
assessments are derived from objectives) but some may more effectively specify
communicative intent. Through a large-scale experiment, we studied three
specification types: learning objectives, insights, and assessments.
Participants, guided by one of these specifications, rated their preferences
for a set of visualization designs. Then, we evaluated the set of visualization
designs to assess which specification led participants to prefer the most
effective visualizations. We find that while all specification types have
benefits over no-specification, each format has its own advantages. Our results
show that learning objective-based specifications helped participants the most
in visualization selection. We also identify situations in which specifications
may be insufficient and assessments are vital.
