Reflection in Theory and Reflection in Practice: An Exploration of the Gaps in Reflection Support among Personal Informatics Apps

Janghee, Cho; Xu, Tian; Zimmermann-Niefield, Abigail; Voida, Stephen

doi:10.1145/3491102.3501991

Cited by 34 publications

(17 citation statements)

References 83 publications

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“…One's goals and needs in tracking can change from being quantified to more qualitative and subjective aspects of self-tracking (Niess & Wo zniak, 2018). These aspects could also yield higher-level reflection about self (Cho et al, 2022). While most STT offers actionable data, we see that they should allow self-trackers to make their own interpretations (Rapp et al, 2019).…”

Section: Towards Meta-informaticsmentioning

confidence: 89%

Data Sensemaking in Self-Tracking: Towards a New Generation of Self-Tracking Tools

Coşkun

Karahanoglu

2022

International Journal of Human–Computer Interaction

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Human-Computer Interaction (HCI) researchers have been increasingly interested in investigating self-trackers' experience with self-tracking tools (STT) to get meaningful insights from their data. However, the literature lacks a coherent, integrated and dedicated source on designing tools that support self-trackers' sensemaking practices. To address this, we carried out a systematic literature review by synthesizing the findings of 91 articles published before 2021 in HCI literature. We identified four data sensemaking modes that self-trackers go through (i.e., self-calibration, data augmentation, data handling, and realization). We also identified four design implications for designing self-tracking tools that support self-trackers' data sensemaking practices (i.e., customized tracking experience, guided sensemaking, collaborative sensemaking, and learning sensemaking through self-experimentation). We provide a research agenda with nine directions for advancing HCI studies on data sensemaking practices. With these contributions, we created an analytical information source that could guide designers and researchers in understanding, studying, and designing for self-trackers' data sensemaking practices.

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Section: Towards Meta-informaticsmentioning

confidence: 89%

Data Sensemaking in Self-Tracking: Towards a New Generation of Self-Tracking Tools

Coşkun

Karahanoglu

2022

International Journal of Human–Computer Interaction

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“…In their influential model, Li et al [44] proposed using 5 stages to design PI systems. Significant research has sought to assist individuals in the latter two stages-reflection and action-when bridging the gap between users' goals and data [17,19,20,54]: these papers emphasize the importance of care in messaging taken between 'positive' and 'negative' nudges, and ongoing reflective support to create the ability for equally nuanced and personal reflections for users. You et al [86] explored these two stages with drivers and their partners, finding that the combination of technology-sensing probes (wearables) with social sense-making (having drivers and their partners share reflections about the drivers' behaviors) was promising for encouraging work-life balance for drivers.…”

Section: Personal Informatics and The Gig Economymentioning

confidence: 99%

Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes

Zhang

Boltz

Lynn

et al. 2023

Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems

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AI technologies continue to advance from digital assistants to assisted decision-making. However, designing AI remains a challenge given its unknown outcomes and uses. One way to expand AI design is by centering stakeholders in the design process. We conduct co-design sessions with gig workers to explore the design of gig worker-centered tools as informed by their driving patterns, decisions, and personal contexts. Using workers' own data as well as city-level data, we create probes-interactive data visuals-that participants explore to surface the well-being and positionalities that shape their work strategies. We describe participant insights and corresponding AI design considerations surfaced from data probes about: 1) workers' well-being trade-offs and positionality constraints, 2) factors that impact well-being beyond those in the data probes, and 3) instances of unfair algorithmic management. We discuss the implications for designing data probes and using them to elevate worker-centered AI design as well as for worker advocacy. CCS CONCEPTS• Human-centered computing → Human computer interaction (HCI).

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“…Fleck and Fitzpatrick [42] discussed various ways that reflection could be supported technologically, e.g., using technology-supported recording for R0 and question-asking for R1, and exploration of alternative perspectives for R2. Using Fleck and Fitzpatrick's framework, a meta-analysis of personal informatics systems by Cho et al [27] found that to encourage transformative reflection, future systems could use reflection prompts that are more focused on the why's of recorded user behaviors.…”

Section: Technological Support For Reflectionmentioning

confidence: 99%

“…Towards this, special attention should be paid to personalizing the reflection process, especially during the first session, to each person's prior experience/expertise and their willingness to engage in reflection. Although using technology to support personalized reflection is nothing new [27], these two aspects have not been properly investigated. Thinking of the reflection process as a framework, customizable components could include (i) the digital media to reflect upon, (ii) the reflection method (e.g., question-asking, emotion-focused or cognition-focused) and (iii) the sequence of reflection-related activities (e.g., pre-video questions before tagging video).…”

Section: Design and Technological Implicationsmentioning

confidence: 99%

“We need to do more... I need to do more”: Augmenting Digital Media Consumption via Critical Reflection to Increase Compassion and Promote Prosocial Attitudes and Behaviors

Lee

Davila

Cheng

et al. 2023

Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems

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Much HCI research on prompting prosocial behaviors focuses on methods for increasing empathy. However, increased empathy may have unintended negative consequences. Our work offers an alternative solution that encourages critical reflection for nurturing compassion, which involves motivation and action to help others. In a between-subject experiment, participants (N=60) viewed a climate change documentary while receiving no prompts (CON), reflective prompts to focus on their emotions (RE) or surprises (RS). State compassion, critical reflection, and motivation to act or learn were measured at the end of the session (post-video) and two weeks later (follow-up). Despite participants' condition not affecting compassion, critical reflection was positively correlated with post-video state compassion. RE and RS participants demonstrated deeper reflection and reported higher motivation to learn post-video, and more prosocial behavioral changes during follow-up. RS participants reported better follow-up recall than RE participants. We conclude by discussing implications on designing technology to support compassion and longer-term critical reflection.

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Reflection in Theory and Reflection in Practice: An Exploration of the Gaps in Reflection Support among Personal Informatics Apps

Cited by 34 publications

References 83 publications

Data Sensemaking in Self-Tracking: Towards a New Generation of Self-Tracking Tools

Data Sensemaking in Self-Tracking: Towards a New Generation of Self-Tracking Tools

Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes

“We need to do more... I need to do more”: Augmenting Digital Media Consumption via Critical Reflection to Increase Compassion and Promote Prosocial Attitudes and Behaviors

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