Roboviz: A Game-Centered Project for Information Visualization Education.

Due to their pedagogical advantages, large final projects in information visualization courses have become standard practice. Students take on a client-real or simulated-a dataset, and a vague set of goals to create a complete visualization or visual analytics product. Unfortunately, many projects suffer from ambiguous goals, over or under-constrained client expectations, and data constraints that have students spending their time on non-visualization problems (e.g., data cleaning). These are important skills, but are often secondary course objectives, and unforeseen problems can majorly hinder students. We created an alternative for our information visualization course: Roboviz, a real-time game for students to play by building a visualization-focused interface. By designing the game mechanics around four different data types, the project allows students to create a wide array of interactive visualizations. Student teams play against their classmates with the objective to collect the most (good) robots. The flexibility of the strategies encourages variability, a range of approaches, and solving wicked design constraints. We describe the construction of this game and report on student projects over two years. We further show how the game mechanics can be extended or adapted to other game-based projects.

Affective Learning Objectives for Communicative Visualizations.

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.

Learning Objectives, Insights, and Assessments: How Specification Formats Impact Design.

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.

Reasoning Affordances with Tables and Bar Charts.

A viewer's existing beliefs can prevent accurate reasoning with data visualizations. In particular, confirmation bias can cause people to overweigh information that confirms their beliefs, and dismiss information that disconfirms them. We tested whether confirmation bias exists when people reason with visualized data and whether certain visualization designs can elicit less biased reasoning strategies. We asked crowdworkers to solve reasoning problems that had the potential to evoke both poor reasoning strategies and confirmation bias. We created two scenarios, one in which we primed people with a belief before asking them to make a decision, and another in which people held pre-existing beliefs. The data was presented as either a table, a bar table, or a bar chart. To correctly solve the problem, participants should use a complex reasoning strategy to compare two ratios, each between two pairs of values. But participants could also be tempted to use simpler, superficial heuristics, shortcuts, or biased strategies to reason about the problem. Presenting the data in a table format helped participants reason with the correct ratio strategy while showing the data as a bar table or a bar chart led participants towards incorrect heuristics. Confirmation bias was not significantly present when beliefs were primed, but it was present when beliefs were pre-existing. Additionally, the table presentation format was more likely to afford the ratio reasoning strategy, and the use of ratio strategy was more likely to lead to the correct answer. These findings suggest that data presentation formats can affect affordances for reasoning.