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“Data storytelling” is described in a variety of ways in literature, and even within the same project what constitutes a “data story” can vary among learners. These different treatments are likely to support different engagements with data, and therefore different learning opportunities for students. Here, we describe preliminary efforts to characterize the variety of ways in which data stories may differ in their mode (e.g., story about work with data and story about the data’s implications) and in their features (e.g., attention to data source; attention to history; case vs aggregate reasoning). To illustrate, we present an analysis of two data story artifacts produced by adolescents that participated in the same data storytelling workshop focused on health and the environment. 

Climate change is a pressing societal challenge. It is also a pedagogical challenge and a worldwide phenomenon, whose local impacts vary across different locations. Climate change reflects global inequity; communities that contribute most to emissions have greater economic resources to shelter from its consequences, while the lowest emitters are most vulnerable. It is scientifically complex, and simultaneously evokes deep emotions. These overlapping issues call for new ways of science teaching that center personal, social, emotional, and historical dimensions of the crisis. In this article, we describe a middle school science curriculum approach that invites students to explore large-scale data sets and author their own data stories about climate change impacts and inequities by blending data and narrative texts. Students learn about climate change in ways that engage their personal and cultural connections to place; engage with complex causal relationships across multiple variables, time, and space; and voice their concerns and hopes for our climate futures. Connections to relevant science, data science, and literacy standards are outlined, along with relevant data sets and assessments. 

This inquiry is guided by a curiosity around the stories that teachers tell about their students, content, and pedagogical approaches focused on data and computational literacies. We present a form of storytelling with theory as we apply theories of syncretism and translanguaging to empirical vignettes about teachers’ sensemaking. We also present a form of storytelling of theory, drawing on teachers’ stories to help us better understand how these theories are related to each other. We bring two teachers’ stories into conversation: one from the Writing Data Stories (WDS) project and the other from the Participating in Literacies and Computer Science (PiLa-CS) project. Both projects utilized translanguaging and syncretism in their conceptions and designs, working with teachers to design for expansive forms of data-based and computational literacies. 

Data science has increasingly integrated sociocritical theories and approaches, helping youth to not only learn data science but also relate data to their everyday and sociohistorical lives. Our project, Writing Data Stories, furthers these efforts by exploring sociocritical data literacies in a large-scale classroom enactment. We examine trends in middle school science student groups’ (n=11) data participation and sociocritical participation, showing how these forms of participation ebb and flow across a 21-day unit. We then present focal group case studies to further unpack how participation shifted over time and suggest what factors contributed to these shifts. We found that data participation was affected by the tools at students’ disposal, and sociocritical participation was shaped by the questions groups asked of each other and the data. These findings suggest that special attention to tools and guiding questions is critical when designing for sociocritical data literacy in middle school science contexts. 

We report on a curriculum development project in which students explore environmental racism through data. Recognizing that quantitative data alone is insufficient to understand the sociohistorical contexts of racism, we draw from syncretic approaches to learning that put everyday experiences and qualitative evidence into direct conversation with quantitative datasets through storytelling. Through two focal cases, we demonstrate how one student leveraged personal experience to engage in deep integrative analysis of data, while another with fewer perceived personal connections to environmental racism focused more specifically on patterns, with less structural or racial analysis. Implications of the analysis include the need to carefully attend to the use of quantitative data related to race and to scaffold the integration of other sources of information with quantitative data sets. 

There is growing interest in how to better prepare K–12 students to work with data. In this article, we assert that these discussions of teaching and learning must attend to the human dimensions of data work. Specifically, we draw from several established lines of research to argue that practices involving the creation and manipulation of data are shaped by a combination of personal experiences, cultural tools and practices, and political concerns. We demonstrate through two examples how our proposed humanistic stance highlights ways that efforts to make data personally relevant for youth also necessarily implicate cultural and sociopolitical dimensions that affect the design and learning opportunities in data-rich learning environments. We offer an interdisciplinary framework based on literature from multiple bodies of educational research to inform design, teaching and research for more effective, responsible, and inclusive student learning experiences with and about data.