Making interpretations visible for design researchers
Masters of Graphic Design
Design Research
North Carolina State University
2015-2016
This visual study explores how a design researcher can interpret ambiguous, qualitative data generated from the community during a participatory design session, such as a video clip of a community member working with a physical, 3D model of her neighborhood. This is 1 of 3 visual studies in the prototyping stage.
My design research has been evolving through studio work, writing, and teaching at NC State, however, it technically began eight years ago when I started working as a graphic designer among urban planners, architects, and engineers.
We all engaged in a similar design process but methods, terminology, and visual conventions varied significantly. This generated rich layers of information, but it was challenging to identify where concepts overlapped and themes formed.
This is when I first started looking at how multi- and interdisciplinary design teams gather, interpret, and visualize data and led me to my research today:
How can visualizations externalize an individual's interpretation of qualitative data that has been generated by the community, public, or participant?
My research is positioned in the overlap of three main ideas:
Qualitative data differs from quantitative in that it often does not have an inherent form or value; its meaning is actively chosen by the researcher through interpretation. The goal of the interpretation is to resolve the ambiguity of qualitative data.
(Drucker, 2015; Sanders and Stappers, 2013; Storkerson, 2011; Hall, 1997)
"Designing" can be considered a process of translation, whereby a person’s ideas, desires, attitudes, or opinions are translated into another form. There are varying degrees of interpretive freedom that go into this process. When we consider participatory design sessions that engage end-users in the design process through the use of 'generative tools' (the context for my investigation) the articulations generated by the participant are essentially translations.
(Simeone, Secundo, and Schiuma, 2015; Diedrich, 2013; Sanders and Stappers, 2013)
Our activities can be broken-down into actions and our actions can be broken down into operations; our activities are directed by motives. Actions are often conscious, where as operations are unconscious and routine. I am arguing for making the unconscious activity of interpretation conscious, or making that internal activity an external activity. Activity theory emphasizes that it is the constant transformation between the external and the internal that is the basis of human activity.
(Kaptelinin and Nardi, 1996)
Information visualizations aid in the analysis of data by triggering high-level cognitive process such as pattern recognition.
(Patterson et al, 2014; Tory and Möller, 2014; Marttila and Kohtala, 2010)
My hypothesis is that by making the activity of interpretation visible to all team members through the aid of a digital visualization tool, it will encourage team members to move out of their own disciplinary biases and conventions, ultimately establishing conditions for the analysis and evaluation of qualitative data so that design opportunities are aligned with the community's needs. My investigation also explores how alternate medias can be used to code ambiguous data, such as emotion.
Research methodology (to date)
Theoretical inquiry: information visualization, qualitative data vs. qualitative data, semiotics and representation/signification, design-as-translation, activity theory
Precedent studies: digital humanities, cartography and mapping, storyboarding, documenting learning
Exploratory research: semi-structured interviews and surveys of designers and design researchers who design with their user to gain an understanding of their design process, and the tools they use to interpret and analyze data
Case studies: QDA tools (NVivo, MAXQDA, ATLAS.ti, Transana); BIM; GIS; on-the-wall analysis; storyboarding tools (Powerpoint, Keynote); spreadsheet-to-visualization tools (Microsoft Excel, Google Charts, RAW)
Persona development: based on data gathered from exploratory research and case studies
User-journey mapping and task analysis
Prototyping: visual studies based on 3 different types of qualitative data: video clip, image collection, and discussion transcript (text-based)
Committee
Dr. Deborah Littlejohn, Chair
Dr. Derek Ham
Kermit Bailey
North Carolina State University
College of Design
A summary of the personas in the interdisciplinary design team in this investigation, based on interviews and surveys of design researchers and practitioners.
The user journey of an interdisciplinary design team who designs with their user. This journey map depicts the team's journey from when they define the problem to when they identify the design opportunity, which is the focus of my investigation.
A visualization of the touch point where a design researcher interprets qualitative data generated by a community member in a participatory design session.
A deconstruction of the internal activity of interpretation. The motive for this activity is to resolve ambiguity in qualitative data that has been generated by a community member in a participatory design session. My research explores how a digital tool can externalize this activity and make it visible to other team members. (Based on activity theory as it relates to interaction design, according to Kaptelinin and Nardi, 1996)
Using Stuart Hall's 'systems of representation,' (1997) we start to see how articulations generated from participatory design sessions — such as physical models or sketches — are signs that stand for something else, be it a hope, desire, or future need of a community member.
Through a series of visual studies, I am exploring how a digital visualization tool can externalize an individual's interpretation of participant-generated qualitative data (PGQD), so that the interpretation is visible to all team members. My hypothesis is that externalizing this activity will prevent oversimplified interpretations and trite assumptions, and preserve the integrity and richness of the data.