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There is a need for a framework that conceptualizes data science learning at the K-12 level which could serve as a guide to policy makers, practitioners and researchers alike. In an attempt to build such a framework, the Concord Consortium and Data Science 4 Everyone joined together, with seed funding from NSF and the Valhalla Foundation to facilitate a series of workshops across the field with the goal of building consensus Learning Progressions (LP) for K-12 DSE. 

Not AvailableAn emerging body of work in the learning sciences has examined how computational models can support teachers in responding to students' prompts, inquiry, and ideas. This work has highlighted how teachers make discursive moves in relation to computational models to support classroom discussion. In this paper, we focus on a complementary phenomenon: teachers' design of code reflections, or curricular modifications that deepen students' engagement with one another's code for scientific and computational sensemaking. We highlight how these code reflections advanced student discourse and how both the code reflections and discourse became more sophisticated over time, shifting towards making connections across code, behaviors, simulation outcomes, data and the scientific process being represented. We reflect on how this progression was driven by shifts in the teachers’ comfort with code and computational modeling and the resources designers can offer to educators to support the development of code reflections. 

Computational modeling tools present unique opportunities and challenges for student learning. Each tool has a representational system that impacts the kinds of explorations students engage in. Inquiry aligned with a tool’s representational system can support more productive engagement toward target learning goals. However, little research has examined how teachers can make visible the ways students’ ideas about a phenomenon can be expressed and explored within a tool’s representational system. In this paper, we elaborate on the construct of ontological alignment—that is, identifying and leveraging points of resonance between students’ existing ideas and the representational system of a tool. Using interaction analysis, we identify alignment practices adopted by a science teacher and her students in a computational agent-based modeling unit. Specifically, we describe three practices: (1) Elevating student ideas relevant to the tool’s representational system; (2) Exploring and testing links between students’ conceptual and computational models; and (3) Drawing on evidence resonant with the tool’s representational system to differentiate between theories. Finally, we discuss the pedagogical value of ontological alignment as a way to leverage students’ ideas in alignment with a tool’s representational system and suggest the presented practices as exemplary ways to support students’ computational modeling for science learning. 

Abstract When learning about scientific phenomena, students are expected tomechanisticallyexplain how underlying interactions produce the observable phenomenon andconceptuallyconnect the observed phenomenon to canonical scientific knowledge. This paper investigates how the integration of the complementary processes of designing and refining computational models using real‐world data can support students in developing mechanistic and canonically accurate explanations of diffusion. Specifically, we examine two types of shifts in how students explain diffusion as they create and refine computational models using real‐world data: a shift towards mechanistic reasoning and a shift from noncanonical to canonical explanations. We present descriptive statistics for the whole class as well as three student work examples to illustrate these two shifts as 6th grade students engage in an 8‐day unit on the diffusion of ink in hot and cold water. Our findings show that (1) students develop mechanistic explanations as they build agent‐based models, (2) students' mechanistic reasoning can co‐exist with noncanonical explanations, and (3) students shift their thinking toward canonical explanations after comparing their models against data. These findings could inform the design of modeling tools that support learners in both expressing a diverse range of mechanistic explanations of scientific phenomena and aligning those explanations with canonical science. 

“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. 

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.