Beyond Problem Solving: Framing and Problem-Solution Co-Evolution in Data Visualization Design

2025-08-09

AI Summary

Existing visualization design models overemphasize technical solution-finding while neglecting the dynamic construction of problem framing and reflective practice. Method: Through a mixed-methods approach—including design challenges, reflective diaries, and semi-structured interviews—we conducted reflexive thematic analysis with 11 visualization experts to examine how problem and solution spaces co-evolve in expert practice. Contribution/Results: We reconceptualize “framing” as an ongoing, situated activity—not merely an initial step—and identify key problem-reframing strategies: metaphorical transfer, heuristic probing, and sketch-based iteration. The study extends visualization design theory by integrating explanatory judgment, ethical narrative, and systematic reflective practice into the design framework. This shifts the paradigm from “problem-solving–oriented” to “meaning-making–oriented” design, emphasizing iterative sensemaking, epistemic responsibility, and context-sensitive interpretation throughout the design process.

Technology Category

• Knowledge Representation and Reasoning: Diagnosis and Abductive Reasoning
• Data Mining & Knowledge Management: Data Visualization & Summarization
• Search and Optimization: Metareasoning and Metaheuristics

Application Category

• Web Mining and Content Analysis: Web data visualization
• User Modeling, Personalization and Recommendation: Accountability, Transparency, and Ethics for personalization
• Responsible Web: Consent frameworks and practices on the web

Abstract

Visualization design is often described as the process of solving a well-defined problem by navigating a design space. While existing visualization design models have provided valuable structure and guidance, they tend to foreground technical problem-solving and underemphasize the interpretive, judgment-based aspects of design. In contrast, research in other design disciplines has emphasized the importance of framing--how designers define and redefine what the problem is--and the co-evolution of problem and solution spaces through reflective practice. These dimensions remain underexplored in visualization research, particularly from the perspective of expert practitioners. This paper investigates how visualization designers frame problems and navigate the dynamic interplay between problem understanding and solution development. We conducted a mixed-methods study with 11 expert practitioners using design challenges, diary entries, and semi-structured interviews. Through reflexive thematic analysis, we identified key strategies that participants used to frame problems, reframe them in response to evolving constraints or insights, and build bridges between problem and solution spaces. These included using metaphors, heuristics, sketching, primary generators, and reflective evaluation of failed or incomplete ideas. Our findings contribute an empirically grounded account of visualization design as a reflective, co-evolutionary practice, where framing is not a preliminary step but a continuous activity embedded in design. Participants often reshaped their understanding of the problem based on solution attempts, tool feedback, and ethical or narrative concerns. These insights extend current visualization design models and highlight the need for frameworks that better account for framing and interpretive judgment. (See paper for full abstract.)

Problem

• Explores how visualization designers frame and redefine design problems
• Investigates co-evolution of problem and solution spaces in visualization
• Identifies strategies for bridging problem understanding and solution development

Innovation

• Framing and reframing problems dynamically
• Co-evolution of problem and solution spaces
• Reflective practice with metaphors and sketching