Entangling the Roles of Maker and Interpreter in Interpersonal Data Narratives

Friske, Mikhaila; Wirfs-Brock, Jordan; Devendorf, Laura

doi:10.1145/3357236.3395442

Cited by 32 publications

(12 citation statements)

References 60 publications

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“…E. Fox et al, 2020;Heikkil€ a et al, 2018;Lupton et al, 2018;Niess et al, 2020;Pink et al, 2018;Pols et al, 2019;Potapov & Marshall, 2020;Rapp & Tirabeni, 2018;Rapp et al, 2019;Rooksby et al, 2014;Smith & Vonthethoff, 2017;Spotswood et al, 2020;Sumartojo et al, 2016;Thudt et al, 2018) Data handling Data curation How can I make the data ready for my interpretation? (Abtahi et al, 2020;Ayobi et al, 2020;Choe et al, 2014;Crawford et al, 2015;Figueiredo et al, 2017;Friske et al, 2020;Gulotta et al, 2015;Khot et al, 2014;Lee et al, 2015;Liang et al, 2016;Mishra et al, 2019;Petelka et al, 2020;Pink et al, 2018;Prioleau et al, 2020;Raj et al, 2017;Rapp, 2018;Rapp & Tirabeni, 2018;Rooksby et al, 2014;Shaer et al, 2016;Sharon & Zandbergen, 2017;Snyder et al, 2019;Thudt et al, 2018;Young & Miller, 2019) Identifying relevant information for data exploration…”

Section: Knowledge Acquisitionmentioning

confidence: 99%

“…The data collected by STT is not always ready for sensemaking, requiring self-trackers to process it for their own analysis. Since the same data might have different meanings to different individuals (Raj et al, 2017) and is open to different interpretations (Friske et al, 2020), processing data is vital when the tracked behavior or condition is very personal (e.g., Vulvodynia reported in Young & Miller, 2019). One way to prepare personal data for sensemaking is data curation.…”

Section: Data Handlingmentioning

confidence: 99%

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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: Knowledge Acquisitionmentioning

confidence: 99%

Section: Data Handlingmentioning

confidence: 99%

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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“…We change the colour of the yarn on the first of each month so that we will be able to see changes and fluctuations throughout the year. In previous work researchers have used crafted data stories to initiate discussion on the invisible data sources in our lives such as everyday sounds [1], autobiographical experiences [5], as well as helping us create memoirs of difficult experiences [2,3]. Hand crafts in themselves can help create markers of time, and previous research in hand knitting has highlighted how these crafted results can become a record of the process [8,9].…”

Section: Implementation and Installationmentioning

confidence: 99%

“…So currently, there is a small run (like the run in a stocking) in the month of July, and there will likely be other small imperfections that occur throughout the year. Other researchers have also discovered the co-authoring element of working with our physical materials when making data physicalizations [5]. Data physicalizations and materials have unique constraints, such as gravity, that data visualizations on paper or screen do not have to contend with [10].…”

Section: Maintenancementioning

confidence: 99%

The Life of a Building

Jones

Grip²,

Kourtoukov³

2022

Sixteenth International Conference on Tangible, Embedded, and Embodied Interaction

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During the pandemic many individuals turned to handcrafts such as knitting to cope with the uncertainty and anxiety we were collectively feeling. In this artwork we wanted to bring knitting out of isolation and to use it as a method of community participation to document the "recovery" year at a local art gallery. In July 2021 we launched The Life of a Building at the Ottawa Art Gallery in Ottawa, Canada, which knits visitor data at the physical gallery as well as online interactions on the gallery website. Each month the colour of the yarn changes, creating a tangible, soft record of this unpredictable year. During TEI, the audience will be able to visit the gallery website, click a button to create their own row of stitches on the machine, and watch the knitting machine knit them into the installation through the livestream. CCS CONCEPTS• Human-centered computing → Visualization systems and tools.

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“…Unlike previous work [46,18,7], the generative sessions were integral to the autoethnographic exploration. Furthermore, that lengthy and detailed sense making process contributes to the corpus or research [9,30,12] that aims at understanding how people make sense of their data using qualitative methods, but also, reflects a typical Research through Design process [24].…”

Section: Introductionmentioning

confidence: 99%

Opportunities and Challenges for Reflective Data-Objects in Long-Distance Relationships

Karyda

Lucero

2021

Human-Computer Interaction – INTERACT 2021

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Personal data representations have been used to support acts of self-reflection, a topic that has received little attention in the context of long-distance relationships (LDRs). To explore a design space for reflective data representations in the LDR context, first-person methods have been employed together with nine generative sessions with people who had been or were in LDRs. Unlike previous work, the generative sessions were part of an autoethnographic exploration. The participants interpreted the first author's visualizations, sketched their own visualizations, and imagined their data as data objects. The insights from those sense-making sessions were then analyzed in a card sorting activity between the first author and their partner where seven themes around communication of long-distance couples emerged. Furthermore, based on the data-object ideas and the various sense making sessions, design opportunities and challenges are drawn related to the transformative nature of relationships, negative reflection and aspects of privacy. In conclusion, personal data are seen as co-evolving with humans constantly transforming people's impression of their romantic relationships in mundane environments.

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Entangling the Roles of Maker and Interpreter in Interpersonal Data Narratives

Cited by 32 publications

References 60 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

The Life of a Building

Opportunities and Challenges for Reflective Data-Objects in Long-Distance Relationships

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