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

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.