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When designing communicative visualizations, we often focus on goals that seek to convey patterns, relations, or comparisons (cognitive learning objectives). We pay less attention to affective intents–those that seek to influence or leverage the audience's opinions, attitudes, or values in some way. Affective objectives may range in outcomes from making the viewer care about the subject, strengthening a stance on an opinion, or leading them to take further action. Because such goals are often considered a violation of perceived ‘neutrality’ or are ‘political,’ designers may resist or be unable to describe these intents, let alone formalize them as learning objectives. While there are notable exceptions–such as advocacy visualizations or persuasive cartography–we find that visualization designers rarely acknowledge or formalize affective objectives. Through interviews with visualization designers, we expand on prior work on using learning objectives as a framework for describing and assessing communicative intent. Specifically, we extend and revise the framework to include a set of affective learning objectives. This structured taxonomy can help designers identify and declare their goals and compare and assess designs in a more principled way. Additionally, the taxonomy can enable external critique and analysis of visualizations. We illustrate the use of the taxonomy with a critical analysis of an affective visualization.

This article explores a contextual computing approach to personalized Internet search efficiency. Contextual computing refers to the enhancement of a user's interactions by understanding the user, the context, and the applications and information being used, typically across a wide set of user goals. In this article, the evolution of the field of information retrieval (IR) was reviewed, setting the stage for examining how a search can be personalized, with particular emphasis on the Web. The Outride system was also described and a set of experiment was reviewed. The Outride system was designed to be a generalized architecture for the personalization of search across a variety of information ecologies. Outride, with eTesting Labs as an independent tester, designed a series of empirical tests to measure if the Outride system makes searches faster and easier to complete. A new type of IR system that personalizes the search experience for each user across their interactions was presented in this article. Initial evidence is also shown to support that contextualized computing approach toward the personalization of search is the next frontier toward significantly increasing search efficiency.