Understanding self-reflection: how people reflect on personal data through visual data exploration
Abstract
Rapid advancements in consumer technologies enable people to collect a wide range of personal data. With a proper means for people to ask questions and explore their data, longitudinal data feeds from multiple self-tracking tools pose great opportunities to foster deep self-reflection. However, most self-tracking tools lack support for self-reflection beyond providing simple feedback. Our overarching goal is to support self-trackers in reflecting on their data and gaining rich insights through visual data exploration. As a first step toward the goal, we built a web-based application called Visualized Self, and conducted an in-lab think-aloud study (N = 11) to examine how people reflect on their personal data and what types of insights they gain throughout the reflection. We discuss lessons learned from studying with Visualized Self, and suggest directions for designing visual data exploration tools for fostering self-reflection.
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Emotional analytics is perceived as a key application by many users.
“Emotional analytics is perceived as a key application by many users [16,17,18,39,90].”
From Progressive Disclosure: When, Why, and How Do Users Want Algorithmic Transparency Information? (2020) · cites this paper as [17]
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- Authors
- Choe, E. K., Lee, B., Zhu, H., Riche, N. H., & Baur, D.
- Affiliations
- Pennsylvania State University (US)
- Microsoft (United States) (US)
- Independent Researcher, Munich, Germany
- Venue
- Proceedings of the 11th EAI International Conference on Pervasive Computing Technologies for Healthcare
- Publisher
- ACM
- Published
- 2017-05-23
- Pages
- 173-182
- ISSN
- 2153-1633 (ISSN-L)
- OpenAlex
- W2610904967
- Cited by
- 194
- References
- 31
- Citations by year
- 2026: 18 · 2025: 24 · 2024: 29 · 2023: 28 · 2022: 24 · 2021: 23 · 2020: 22 · 2019: 9 · 2018: 16 · 2017: 1
- FWCI
- 9.92
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- International Conference on Pervasive Computing Technologies for Healthcare (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Human-Computer Interaction › Innovative Human-Technology Interaction
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Data Visualization and Analytics
- Social Sciences › Decision Sciences › Information Systems and Management › Personal Information Management and User Behavior
- Keywords
- self-reflection, self-tracking, personal data, visual data exploration, longitudinal data, data visualization tools, think-aloud study
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