Affinity Lens - Data-Assisted Affinity Diagramming with Augmented Reality
CHI, pp. 3982019.
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Abstract:
Despite the availability of software to support Affinity Diagramming (AD), practitioners still largely favor physical sticky-notes. Physical notes are easy to set-up, can be moved around in space and offer flexibility when clustering un-structured data. However, when working with mixed data sources such as surveys, designers often trade o...More
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Introduction
- Affinity Diagrams (AD) and related approaches are the method of choice for many designers and UX researchers.
- AD supports analysis and synthesis of interview notes, brainstorming, creating user personas, and evaluating interactive prototypes [24].
- Notes can be placed on walls or surfaces in a way that leverages spatial cognition, offers flexibility in grouping and clustering, and physically persists
- Both individuals and groups can participate on large shared surfaces.
- AD users work to derive structure from inherently fuzzy and seemingly unstructured input.
Highlights
- Affinity Diagrams (AD) and related approaches are the method of choice for many designers and UX researchers
- We used the same task and study protocol as in section 3, but instead of having the data directly printed on the notes, we added an ArUco marker to bind the note to a data row
- To encourage discussion between participants, we only provided a single Android mobile device (5.5. inches,1440 x 2560 pixels) with Affinity Lens running on the Chrome browser
- As designers are increasingly working with sources of information that consist of both qualitative and quantitative data, they often desire analytical power beyond physical sticky notes
- With Affinity Lens, we have demonstrated how data-assisted affinity diagrams can be implemented with low-cost, mobile devices while maintaining the lightweight benefits of existing Affinity Diagrams practice
- We have only lightly explored the space of lenses, but already, users of the current system were enthusiastic about using Affinity Lens in their current Affinity Diagrams-related work tasks
Methods
- The probe sessions allowed them to identify key tasks for data assistance
- These were used to drive many of Affinity Lens features.
- The two of five sessions that began clustering using data were less successful in completing tasks.
- They took a lot longer to analyze text within each cluster and to interpret how the text and data made sense as a whole.
- Though it would be relatively easy to implement, Affinity Lens does not, for example, suggest initial clusters
Results
- To evaluate Affinity Lens, the authors conducted two different in-lab AD studies.
- The first was a controlled study in which the authors determined whether end-users could effectively generate data insights using Affinity Lens.
- In the second study, which was open-ended, the authors aimed to evaluate Affinity Lens in a realistic AD workflow.
- The authors conducted three 90-minute sessions with four HCI design student (P1P4) and two UX professionals (P5-P6).
- Inches,1440 x 2560 pixels) with Affinity Lens running on the Chrome browser
- To encourage discussion between participants, the authors only provided a single Android mobile device (5.5. inches,1440 x 2560 pixels) with Affinity Lens running on the Chrome browser
Conclusion
- DISCUSSION AND FUTURE
WORK
There is clearly a need for integrated sensemaking from qualitative and quantitative data when conducting mixedmethods research. - Through Affinity Lens’s AR overlays, the authors demonstrated how DAAD can enrich the analysis experience of survey data, a typical use-case within HCI research.
- HCI work uses interaction logs, sensor streams, and multimedia content to inform system design and end-user behavior.
- One can augment the text from think-aloud transcripts with interaction logs showing mouse clicks data, or overlay raw video footage of actual task execution for multiple participants in parallel.Affinity diagrams are used throughout academic and business communities as part of the design process.
- The authors have only lightly explored the space of lenses, but already, users of the current system were enthusiastic about using Affinity Lens in their current AD-related work tasks
Summary
Introduction:
Affinity Diagrams (AD) and related approaches are the method of choice for many designers and UX researchers.- AD supports analysis and synthesis of interview notes, brainstorming, creating user personas, and evaluating interactive prototypes [24].
- Notes can be placed on walls or surfaces in a way that leverages spatial cognition, offers flexibility in grouping and clustering, and physically persists
- Both individuals and groups can participate on large shared surfaces.
- AD users work to derive structure from inherently fuzzy and seemingly unstructured input.
Methods:
The probe sessions allowed them to identify key tasks for data assistance- These were used to drive many of Affinity Lens features.
- The two of five sessions that began clustering using data were less successful in completing tasks.
- They took a lot longer to analyze text within each cluster and to interpret how the text and data made sense as a whole.
- Though it would be relatively easy to implement, Affinity Lens does not, for example, suggest initial clusters
Results:
To evaluate Affinity Lens, the authors conducted two different in-lab AD studies.- The first was a controlled study in which the authors determined whether end-users could effectively generate data insights using Affinity Lens.
- In the second study, which was open-ended, the authors aimed to evaluate Affinity Lens in a realistic AD workflow.
- The authors conducted three 90-minute sessions with four HCI design student (P1P4) and two UX professionals (P5-P6).
- Inches,1440 x 2560 pixels) with Affinity Lens running on the Chrome browser
- To encourage discussion between participants, the authors only provided a single Android mobile device (5.5. inches,1440 x 2560 pixels) with Affinity Lens running on the Chrome browser
Conclusion:
DISCUSSION AND FUTURE
WORK
There is clearly a need for integrated sensemaking from qualitative and quantitative data when conducting mixedmethods research.- Through Affinity Lens’s AR overlays, the authors demonstrated how DAAD can enrich the analysis experience of survey data, a typical use-case within HCI research.
- HCI work uses interaction logs, sensor streams, and multimedia content to inform system design and end-user behavior.
- One can augment the text from think-aloud transcripts with interaction logs showing mouse clicks data, or overlay raw video footage of actual task execution for multiple participants in parallel.Affinity diagrams are used throughout academic and business communities as part of the design process.
- The authors have only lightly explored the space of lenses, but already, users of the current system were enthusiastic about using Affinity Lens in their current AD-related work tasks
Related work
- Affinity diagramming (also known as the KJ Method) has been used extensively for over 50 years [42]. AD supports organizing and making sense of unstructured qualitative data through a bottom-up process. A schema is developed by individuals, or groups, who arrange and cluster paper notes based on similarity of content, i.e., affinity. Because of its wide use, several projects have worked to address the shortcomings of the basic, ‘pen-and-paper’ use. These have centered around several areas including remote collaboration, clusters creation assistance, explicit and implicit search mechanisms, general visual analytics systems, and systems to bridge digital and paper documents. We briefly touch upon each area to set the context for the Affinity Lens project.
Funding
- Proposes Affinity Lens, a mobile-based augmented reality application for Data-Assisted Affinity Diagramming
- Developed design principles for data-assisted AD and an initial collection of lenses
- Finds that Affinity Lens supports easy switching between qualitative and quantitative ‘views’ of data, without surrendering the lightweight benefits of existing AD practice
- Found that in many cases analysis involved data from surveys , sensor data , and interaction logs
- Identified three main concerns: the affordances of physical notes should be maintained, additional data and insights should be easy to retrieve, and data should be available just-in-time, without disrupting the primary diagramming practice
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