Data Literacies in Informal Settings

Abstract

As data becomes an integral part of our daily lives, the general public increasingly needs to actively engage with it to understand everyday lives, support personal goals, and engage with social issues. Formal data science training, however, remains out of reach for most people and does not cater to the diverse needs associated with data. Emerging informal settings, such as online social spaces and community workshops, offer accessible platforms for diverse and meaningful data interactions. However, current research on data literacy does not fully capture the diverse ways the public interacts with data in these informal environments. The dissertation presents four studies exploring the ways people interact with data in informal settings, examining the challenges and needs emerging from these engagements. These findings can guide future research and shape the design of tools to foster data engagement in diverse informal environments. Study A illustrates a mixed method analysis of 400 Scratch forum discussion threads and more than 240,000 user-made projects that involve data, unpacking the benefits and drawbacks of interest-driven participation in a large online community. Study B presents a semi-structured interview study with 14 Kaggle users on their collaborative and communicative practices in working with large datasets, highlighting the needs and challenges in conveying procedures to a varied audience and fostering collaboration among users of different experience levels. Study C contains a theory-driven quantitative analysis of a large collection of Twitter messages that involve discussions about COVID-19 vaccine data, identifying features that differentiate critical engagement with data from conspiracy discourses. Study D presents a constructionist system that scaffolds novices to programmatically analyze and visualize data, as well as the insights from the user study workshops that showcase the diverse range of concepts, perspectives, and practices that the system can support. Together, these studies reveal a pluralism in people's competencies and epistemological pathways concerning data engagement—what I refer to as "data literacies"—that should be accounted for in the design of technologies and research for data literacies. This dissertation contributes rich empirical knowledge on the public's engagement with data in a range of informal settings, various design recommendations for informal environments to support data literacies, a call for acknowledging of the pluralism in data literacies in the design of tools and interventions, and a sociotechnical framework for conceptualizing and designing to support data literacies in informal settings.