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Data Agents: Promoting Reflection through Meaningful Representations of Personal Data in Everyday Life

Maria Karyda,Elisa D. Mekler,A. Lucero

Published 2021 in International Conference on Human Factors in Computing Systems

ABSTRACT

Visual and physical representations of historical personal data have been discussed as artifacts that can lead to self-reflection through meaning-making. However, it is yet unclear how those two concepts relate to each other. We focus on meaningfulness, a part of meaning-making that relates to feelings. In this paper, we present three projects where mundane objects, our data agents, are combined in meaningful ways with personal data with the aim to trigger reflection by placing a person’s individual experience of data in relation to others’. To identify relationships between self-reflection and meaningfulness we use Fleck and Fitzpatrick’s framework to describe the levels of reflection that we found in our projects and Mekler and Hornbæk’s meaning framework to define the depth of reflection. We conclude with a discussion on four themes in which we outline how data agents informed the intersections between our central concepts. This paper, constitutes a first step towards unpacking those relationships and invites for further explorations by the HCI community.

PUBLICATION RECORD

  • Publication year

    2021

  • Venue

    International Conference on Human Factors in Computing Systems

  • Publication date

    2021-05-06

  • Fields of study

    Computer Science

  • Identifiers
    DOI 10.1145/3411764.3445112
  • External record

    Open on Semantic Scholar

  • Source metadata

    Semantic Scholar

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