PAIRcolator: Pair Collaboration for Sensemaking and Reflection on Personal Data

要旨

This paper explores pair collaboration as a novel approach for making sense of personal data. Pair collaboration---characterized by dyadic comparison and structured roles for questioning and reasoning---has proven effective for co-constructing knowledge. However, current collaborative visualization tools primarily focus on group comparisons, overlooking the challenges of accommodating pair collaboration in the context of personal data. To address this gap, we propose a set of design rationales supporting subjective data analysis through dyadic comparison and mixed-focus collaboration styles for co-constructing personal narratives. We operationalize these principles in a tangible visualization toolkit, \projectname. Our user study demonstrates that pairwise collaboration facilitated by the toolkit: 1) reveals detailed data insights that are effective for recalling personal experiences, and 2) fosters a structured, reciprocal sensemaking process for interpreting and reconstructing personal experiences beyond data insights. Our results shed light on the design rationales for, and the processes of pair sensemaking of personal data, and their effects to foster deep levels of reflection.

著者
Di Yan
Delft University of Technology, Delft, Netherlands
Jacky Bourgeois
Delft University of Technology, Delft, Netherlands
Yen-Chia Hsu
University of Amsterdam, Amsterdam, Netherlands
Gerd Kortuem
Delft University of Technology, Delft, Netherlands
DOI

10.1145/3706598.3713332

論文URL

https://dl.acm.org/doi/10.1145/3706598.3713332

動画

会議: CHI 2025

The ACM CHI Conference on Human Factors in Computing Systems (https://chi2025.acm.org/)

セッション: Personal Data and Decision-Making

Annex Hall F204
6 件の発表
2025-04-28 20:10:00
2025-04-28 21:40:00
日本語まとめ
読み込み中…