  Significant research has provided robust task and evaluation languages for
the analysis of exploratory visualizations. Unfortunately, these taxonomies
fail when applied to communicative visualizations. Instead, designers often
resort to evaluating communicative visualizations from the cognitive efficiency
perspective: "can the recipient accurately decode my message/insight?" However,
designers are unlikely to be satisfied if the message went 'in one ear and out
the other.' The consequence of this inconsistency is that it is difficult to
design or select between competing options in a principled way. The problem we
address is the fundamental mismatch between how designers want to describe
their intent, and the language they have. We argue that visualization designers
can address this limitation through a learning lens: that the recipient is a
student and the designer a teacher. By using learning objectives, designers can
better define, assess, and compare communicative visualizations. We illustrate
how the learning-based approach provides a framework for understanding a wide
array of communicative goals. To understand how the framework can be applied
(and its limitations), we surveyed and interviewed members of the Data
Visualization Society using their own visualizations as a probe. Through this
study we identified the broad range of objectives in communicative
visualizations and the prevalence of certain objective types.
