Publication: Data sensemaking in self-tracking: towards a new generation of self-tracking tools
Program
KU-Authors
KU Authors
Co-Authors
Karahanoglu, Armagan
Advisor
Publication Date
Language
English
Type
Journal Article
Journal Title
Journal ISSN
Volume Title
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.
Description
Source:
International Journal of Human-Computer Interaction
Publisher:
Taylor & Francis Inc
Keywords:
Subject
Computer science, Cybernetics, Ergonomics