On week 3, we shifted gears towards the building mode from the researching mode to digest the information we took in the literature review and but also express our own perspectives existing inside ourselves. We quickly designed a self-tracking app including its data visualization.
Purpose
To cultivate, explore and develop our own perspectives on quantified self by rapidly designing something. It’s called “Build to think” compared with “Think to Build”. Technical constraints or feasibility are not strictly considered this time for not restricting ideation.
Overall Process
By Individuals
- Define a user goal / motivation
- Identify Data types and Data points collected
- Sketch data visualization ideas
- Assume use cases
As a Group
- Share, discuss and reflect each work
Individual Works
The followings are short descriptions of each work. You can jump to individual works by clicking links.
- Sandhini: A symptoms/ feeling diagnosis experience to enable people to decipher the reason behind them feeling a certain way.
- Roxanne: A sleep wellbeing experience that uses abstract data visualizations through sonification and 3D topographies to motivate the user to non-judgmentally explore their data.
- Shin: Walking experience is visualized by a 3D thread human model made of emotions recognized by gait data. Through connected data, the app generates gait and route guidance to manage user emotions.
- Jorge: Thought collection to encourage reflection through a digital pebble collection. Guides users through a topography of their collected thoughts in hopes of better understanding how they make them feel.
Conclusions
After the group discussion, we landed on the perspectives, spectrum we should take into consideration, and questions we have to answer when we design our final concept.
Perspectives
- We are more interested in designing something that helps users discover new identity, connections, or truths in themselves rather than judge themselves through data to optimize a value or achieve their goals.
- Some of our designs brought the inside out (biodata represented as an environment) while others brought the outside in (movement data as an aggregate self) to reveal new ways of inhabiting data.
Spectrum
- We have to pay attention to the balance between data literacy and data legibility that seem to be a trade-off. When we visualize data that can be interpreted in specific ways, we don’t have to educate people on how to read data. On the other hand, when we create more abstract data visualization, we have to take education of data literacy into consideration.
- The spectrum above is also relevant to our design decision on inclusivity. It means not only people with physical or cognitive impairment but also those who are socially, culturally or statistically excluded. Who has been ignored or left behind in quantified self or data visualization?
- Regarding forms of data visualization, how much we would use analogies or statistical charts that are traditional in the field, which is strongly tied to discussions on data literacy and legibility.
- Causality and correlations of data are important points to be considered. Even though we’d like users to discover something new on their identity, personality or behaviors, we should avoid a case that users would interpret correlations among different data, behaviors or personality in a wrong way.
Questions
- How can we gauge a user’s data literacy to modify data interpretability?
- What role does analogy, metaphor, narrative, and ritual play to enhance data visualization and interpretation?
- What are effective techniques for embedding data literacy education in our designs?
- Can we design for a spectrum of user abilities? How would our designs change?
- How is uncertainty represented in non-traditional data visualizations?
