Understanding self-reflection: how people reflect on personal data through visual data exploration

by | May 23, 2017 | Research Publications

Author(s):

  • Eun Kyoung Choe ,
  • Bongshin Lee ,
  • Haining Zhu ,
  • Nathalie Henry Riche ,
  • Dominikus Baur

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.

Documentation:

https://doi.org/10.1145/3154862.3154881

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