Data Bias Recognition in Museum Settings: Framework Development and Contributing Factors

要旨

Critical thinking skills are increasingly important for comprehending our data-rich society. While museums provide data for discussion, visitors may not naturally question data in such displays due to the inherent authority of a museum. To investigate what factors can help visitors recognize bias in data, we interviewed visitors after they interacted with an augmented reality data map in an interactive data exhibition. Here, we present a qualitative analysis of fifteen semi-structured interviews with visitors who engaged with mapped data from the citizen science platform iNaturalist. The study revealed that 47% of participants were able to recognize bias, and familiarity was found to be a significant factor in this ability. We propose a three-layer framework to understand the cognitive processes of bias recognition in informal learning settings and apply this framework to our data to inform future work for designing displays to promote critical engagement with data in free-choice learning contexts.

著者
Stella Quinto Lima
Georgia Institute of Technology, Atlanta, Georgia, United States
Gabriela Buraglia
Georgia Institute of Technology, Atlanta, Georgia, United States
Wong Kam-Kwai
The Hong Kong University of Science and Technology, Hong Kong, China
Jessica Roberts
Georgia Institute of Technology, Atlanta, Georgia, United States
DOI

10.1145/3706598.3714092

論文URL

https://dl.acm.org/doi/10.1145/3706598.3714092

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: Data Interpretation and Storytelling

G418+G419
6 件の発表
2025-04-28 23:10:00
2025-04-29 00:40:00
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