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

Area of Science:

• Data Visualization
• Human-Computer Interaction
• Cognitive Science

Background:

• Specifying communicative intent in visualization design is often ad hoc.
• Effective specification methods are needed to guide visualization creation and selection.
• Previous work framed communicative intent as a learning or assessment problem.

Purpose of the Study:

• To compare the effectiveness of different specification types for guiding visualization design choices.
• To determine which specification format best helps users select effective visualizations.
• To understand the advantages and limitations of learning objectives, insights, and assessments.

Main Methods:

• A large-scale experiment was conducted with participants rating visualization design preferences.
• Participants were guided by one of three specification types: learning objectives, insights, or assessments.
• Visualization designs were evaluated to determine which specification led to the selection of the most effective ones.

Main Results:

• All tested specification types offered benefits over no specification.
• Learning objective-based specifications were most helpful for visualization selection.
• Each specification format demonstrated unique advantages and disadvantages.

Conclusions:

• Learning objectives are a highly effective method for specifying communicative intent in visualization design.
• While learning objectives excel in selection guidance, assessments are crucial for specific design challenges.
• Further research can refine specification languages for improved visualization design processes.