The DOI matches its Crossref record; 31 of the 31 references it cites were found in a registry or an index.
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 [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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Metadata
- 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 Graphics and Computer-Aided Design › 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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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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