s through Visual Data Exploration May 25, 2017 Eun Kyoung Choe 1 , - - PowerPoint PPT Presentation

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s through Visual Data Exploration May 25, 2017 Eun Kyoung Choe 1 , - - PowerPoint PPT Presentation

Jul 25, 2023 377 likes •878 views

PervasiveHealth 2017 Understanding Self-Reflection: How People Reflect on Personal Data s through Visual Data Exploration May 25, 2017 Eun Kyoung Choe 1 , Bongshin Lee 2 , Haining Zhu 1 , Nathalie Henry Riche 2 , Dominikus Baur 3 1 Pennsylvania

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Eun Kyoung Choe 1

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Understanding Self-Reflection: How People Reflect on Personal Data through Visual Data Exploration

PervasiveHealth 2017

May 25, 2017 Eun Kyoung Choe1, Bongshin Lee2, Haining Zhu1, Nathalie Henry Riche2, Dominikus Baur3

1 Pennsylvania State University, 2 Microsoft Research, 3 Independent Researcher

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Eun Kyoung Choe 2

Self-monitoring

An activity of recording one’s own behaviors, thoughts, or feelings

[Kopp, J. (1988) Self-monitoring: A literature review of research and practice]

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Eun Kyoung Choe 3

Self-monitoring from the 19th century

Public scales from the late 1880s in contemporary Paris (from Crawford 2015)

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Eun Kyoung Choe 4

Mental Health Tracker. http://asweatlife.com/2016/08/ideas-fitness-bullet-journal/

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Eun Kyoung Choe 6

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Promises

External measurement to self-knowledge Self-knowledge to self-improvement?

Personal Data Self-Knowledge

?

Self- Improvement?

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Goal

Assessment Self-awareness Self-Reflection Patient Empowerment Behavior Change

Explore Share Collect

Human-Data Interaction for Self-Monitoring

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Eun Kyoung Choe 9

Fawcett (2015)

data exploration and analytics capabilities for personal data analysis “remain surprisingly emain surprisingly primit primitive, leaving t ive, leaving the analyt he analytical heavy l ical heavy lift ifting to ing to the end user he end user”…

Mining the quantified self: personal knowledge discovery as a challenge for data science. [Big Data 2015]

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Personal data visualization

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Stage-based model of PI

Li, Dey, & Forlizzi. (2010)

?

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Visual data exploration

Powerful way to help people reveal meaningful insights about themselves and to facilitate self-reflection

Visual Data Exploration

Data Insight

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The British Diet http://britains-diet.labs.theodi.org/

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Limited support for data exploration

  • Scattered data across multiple platforms (Li et al., 2011; Choe et al., 2014.)
  • Don’t know what to do with the data (Choe et al., 2014; Epstein et al., 2015;

Lazar et al., 2015.)

  • Difficult to translate questions into data attributes (Grammel et

al., 2010; Huang et al., 2015.)

  • Difficult to construct visualizations (Grammel et al., 2010; Huang et al., 2015.)
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Visual Data Exploration

Research questions

RQ1: How do people reflect on their self-tracking data? (Process)

Data Insight

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Research questions

RQ1: How do people reflect on their self-tracking data? (Process) RQ2: What insights do people gain from visual data exploration? (Outcome)

Visual Data Exploration

Data Insight

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Eun Kyoung Choe 17

Insights

A key purpose of visualization “An individual observation about the data by the participant, a unit of analysis” Characteristics of insights Insight gaining process

Saraiya et al., 2005 Yi et al., 2008 North, 2006 Card et al., 2005

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Types of personal insights

30 video recordings of QS presentations [IEEE CG&A 2015]

Eun Kyoung Choe m.c. schraefel Bongshin Lee

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Detail Self-Reflection Trend Comparison Correlation Data Summary Distribution Outlier

Visualization Insights

(74%) (51%) (36%) (35%) (11%) (9%) (6%) (2%)

Characterizing Visualization Insights from Quantified Selfers’ Personal Data Presentation. [CG&A 2015]

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Visual Data Exploration

Research questions

RQ1: How do people reflect on their self-tracking data? (Process) RQ2: What insights do people gain from visual data exploration? (Outcome)

Data Insight

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Visualized Self

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Design Rationales

  • 1. Support data exploration

for the general public

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Design Rationales

  • 2. Design for a personal

data context

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Data integration from multiple sources

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Data import

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Data summary

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Temporal comparison

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Study Session

Invited 11 self-trackers to the lab

  • Have been regularly tracking personal data for two months or longer
  • Have been using two or more of the following devices or apps: Fitbit, Aria, MS

Band, Moves, RunKeeper, RescueTime

  • Age range: 24–60 (mean = 35.8)

Study session (1.5–2 hours total)

  • Demographic / tracking experience survey
  • Tutorial and demonstration of the tool
  • Think-aloud session with observation
  • De-briefing interview
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What a session looks like

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Data Analysis

  • Transcribed the think-aloud session &

debriefing interview

  • Open coding, axial coding on the process of

self-reflection (RQ1)

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Eun Kyoung Choe 33

Data Analysis

  • Transcribed the think-aloud session &

debriefing interview

  • Open coding, axial coding on the process of

self-reflection (RQ1)

  • Directed contents analysis for the types of

insights (RQ2)

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Levels of Reflection

R0—description R1—description with justification R2—exploring relationships R3—asking of fundamental questions R4—considering social and ethical issues

Fleck, R., & Fitzpatrick, G. (2010). Reflection on reflection: framing a design landscape.

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Findings

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Personal insight types

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From a lower-level reflection to a higher-level reflection

“Band has 222 days of collected data and it's saying my average is 1,464, but Fitbit has 81 days and it's saying I have 5,764 as my average. So it leads me to wonder which one is more accurate?” [P7]

Data summary; Comparing multiple services; “R0” reflection Question; “R2” reflection

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Insight gaining pattern #1

Visual data exploration Recall previous contexts that could explain the captured behavior

“R1” reflection

Revisiting with explanation, descriptive reflection

“I think that was soon after my surgery and that maybe would make sense cause I’d have to get up to take medicine and maybe being restless or something.” [P8]

External context

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Insight gaining pattern #2

Recall previous contexts that could explain the captured behavior Create an interesting question / hypothesis Visually explore the data to look for an answer

“R2” reflection

Questioning; exploring relationships

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Temporal comparison

P1: (entering Sept 15, 2015 to compare his weight before and after this date) Researcher: Why Sept 15? P1: “That's kind of around the time I changed jobs. I was wondering if there was anything interesting there.”

External context; Comparison by time segmentation

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Using External Context in Data Exploration

External Context: Uncaptured data provided by the presenter to understand and explain a phenomenon shown in the data Calendar events, location semantics, major life events, key dates, vacation, workout types, seasons, weather…

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Value judgment: “Saturday is pretty bad” [in terms of step count] Making a resolution: “So I need to take action to probably monitor myself to ensure that I’m at least at 2,000 [steps] or more.” [P10]

“R3” reflection

alters or transforms the reflector’s original point of view

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Reflection on the levels of reflection

Many R0, R1, and R2 types of reflections due to Visualized Self’s data summary and temporal comparison pages Drawing higher-level reflections (i.e., R3) was less common R3 might be an important reflection type that can potentially lead to short-term, or even long-term behavior change Did not observe R4

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Summary

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Eun Kyoung Choe 45

Help people capture/use various contextual information Help people create interesting questions and hypotheses Flexible data selection, filtering, and comparison features Combine system-driven and human-driven insights

Supporting self-reflection with VDE

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Eun Kyoung Choe 46

Thank you!

Eun Kyoung Choe (echoe@ist.psu.edu)

faculty.ist.psu.edu/choe

Funding:

National Science Foundation Microsoft Research

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Data ingegration from multiple sources