Publication: Data sensemaking in self-tracking: towards a new generation of self-tracking tools
dc.contributor.coauthor | Karahanoğlu, Armağan | |
dc.contributor.department | Department of Media and Visual Arts | |
dc.contributor.department | KUAR (KU Arçelik Research Center for Creative Industries) | |
dc.contributor.kuauthor | Coşkun, Aykut | |
dc.contributor.schoolcollegeinstitute | College of Social Sciences and Humanities | |
dc.contributor.schoolcollegeinstitute | Research Center | |
dc.date.accessioned | 2025-01-19T10:30:07Z | |
dc.date.issued | 2023 | |
dc.description.abstract | 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. © 2022 Taylor & Francis Group, LLC. | |
dc.description.indexedby | WOS | |
dc.description.indexedby | Scopus | |
dc.description.issue | 12 | |
dc.description.openaccess | All Open Access; Green Open Access | |
dc.description.publisherscope | International | |
dc.description.sponsoredbyTubitakEu | N/A | |
dc.description.volume | 39 | |
dc.identifier.doi | 10.1080/10447318.2022.2075637 | |
dc.identifier.issn | 1044-7318 | |
dc.identifier.quartile | Q1 | |
dc.identifier.scopus | 2-s2.0-85131162077 | |
dc.identifier.uri | https://doi.org/10.1080/10447318.2022.2075637 | |
dc.identifier.uri | https://hdl.handle.net/20.500.14288/25981 | |
dc.identifier.wos | 800467400001 | |
dc.language.iso | eng | |
dc.publisher | Taylor and Francis Ltd. | |
dc.relation.ispartof | International Journal of Human-Computer Interaction | |
dc.subject | Computer science | |
dc.subject | Interdisciplinary applications | |
dc.subject | Green and sustainable science and technology | |
dc.subject | Social sciences | |
dc.title | Data sensemaking in self-tracking: towards a new generation of self-tracking tools | |
dc.type | Journal Article | |
dspace.entity.type | Publication | |
local.contributor.kuauthor | Coşkun, Aykut | |
local.publication.orgunit1 | College of Social Sciences and Humanities | |
local.publication.orgunit1 | Research Center | |
local.publication.orgunit2 | Department of Media and Visual Arts | |
local.publication.orgunit2 | KUAR (KU Arçelik Research Center for Creative Industries) | |
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