Data practices during COVID: Everyday sensemaking in a high‐stakes information ecology

How do people reason with data to make sense of the world? What implications might everyday practices hold for data literacy education? We leverage the unique context of the COVID‐19 pandemic to shed light on these questions. COVID‐19 has engendered a complex, multimodal ecology of information resources, with which people engage in high‐stakes sensemaking and decision‐making. We take a relational approach to data literacy, examining how people navigate and interpret data through interactions with tools and other people. Using think‐aloud protocols, a diverse group of people described their COVID‐19 information‐seeking practices while working with COVID‐19 information resources they use routinely. Although participants differed in their disciplinary background and proficiency with data, they each consulted data frequently and used it to make sense of life in the pandemic. Three modes of interacting with data were examined: scanning, looking closer and puzzling through. In each of these modes, we examined the balance of agency between people and their tools; how participants experienced and managed emotions as part of exploring data; and how issues of trust mediated their sensemaking. Our findings provide implications for cultivating more agentic publics, using a relational lens to inform data literacy education. PRACTITIONER NOTES: : Many people, even those with higher education, struggle with interpreting quantitative data representations. Social and emotional factors influence cognition and learning. People are often overwhelmed by the abundance of available information online. There is a need for data literacy approaches that are humanistic and relational. : Everyday data practices can be variable and adaptable, and include engaging with data at different levels: scanning, looking closer, and puzzling through. Each of these modes involves different data practices. People, independently of their quantitative interpretation skills and disciplinary backgrounds, may engage differently with data (eg, avoiding versus delving deeper) based on their emotional responses, level of trust or interpersonal relationships that are evoked by the data. These everyday data practices have implications for people's sense of their own agency with data and involve emotional and trust‐based relationships that shape their interpretations of data. These relational aspects of data literacy suggest productive directions for data literacy education. : Data literacy can be taught as a process that is inherently relational, for example, by discussing the ways in which learners are personally connected to different data, what emotions these connections evoke, and how that affects the ways in which they attend to, trust and interpret the data. Data literacy education can cultivate a wider range of data practices at a variety of depths of interaction, rather than prioritizing only in‐depth inquiry. It may be helpful to include complex experiences with data sources that require learners to go beyond a binary “trustworthy/untrustworthy” distinction, so that learners can become more strategic, nuanced and intentional in forming a variety of trust relationships with different sources. Discussing how learners' everyday data practices interact with different data representations and tools can help them become more critically aware of the possible purposes, values, and risks associated with their everyday data practices.