Publication:
Data sensemaking in self-tracking: towards a new generation of self-tracking tools

dc.contributor.coauthorKarahanoğlu, Armağan
dc.contributor.departmentDepartment of Media and Visual Arts
dc.contributor.departmentKUAR (KU Arçelik Research Center for Creative Industries)
dc.contributor.kuauthorCoşkun, Aykut
dc.contributor.schoolcollegeinstituteCollege of Social Sciences and Humanities
dc.contributor.schoolcollegeinstituteResearch Center
dc.date.accessioned2025-01-19T10:30:07Z
dc.date.issued2023
dc.description.abstractHuman-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. © 2022 Taylor & Francis Group, LLC.
dc.description.indexedbyWOS
dc.description.indexedbyScopus
dc.description.issue12
dc.description.openaccessAll Open Access; Green Open Access
dc.description.publisherscopeInternational
dc.description.sponsoredbyTubitakEuN/A
dc.description.volume39
dc.identifier.doi10.1080/10447318.2022.2075637
dc.identifier.issn1044-7318
dc.identifier.quartileQ1
dc.identifier.scopus2-s2.0-85131162077
dc.identifier.urihttps://doi.org/10.1080/10447318.2022.2075637
dc.identifier.urihttps://hdl.handle.net/20.500.14288/25981
dc.identifier.wos800467400001
dc.language.isoeng
dc.publisherTaylor and Francis Ltd.
dc.relation.ispartofInternational Journal of Human-Computer Interaction
dc.subjectComputer science
dc.subjectInterdisciplinary applications
dc.subjectGreen and sustainable science and technology
dc.subjectSocial sciences
dc.titleData sensemaking in self-tracking: towards a new generation of self-tracking tools
dc.typeJournal Article
dspace.entity.typePublication
local.contributor.kuauthorCoşkun, Aykut
local.publication.orgunit1College of Social Sciences and Humanities
local.publication.orgunit1Research Center
local.publication.orgunit2Department of Media and Visual Arts
local.publication.orgunit2KUAR (KU Arçelik Research Center for Creative Industries)
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