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Rivulet is a framework and collection of Python Jupyter notebooks for fetching pedagogically generative scientific datasets. They provide executable templates for accessing, filtering, and evaluating data from public databases using Application Programming Interfaces (APIs) and expert insights regarding quantitative features of scientific interest. Our primary goal is to support educators and curriculum designers finding data that support meaningful statistical and scientific engagement through relevance to both students and to the domain. 

This tech demo introduces key enhancements to MoDa: A free, open-source web based modeling and data analysis system designed to support students in making sense of complex systems. MoDa is a domain-specific, block-based computational modeling tool that allows students to build models side-by-side to real-world data. Our latest enhancements include integrating quantitative data in the system using the Common Online Data Analysis Platform and Activity Player to support structured data-rich investigations, allowing students to use a wider variety of real-world data sources to refine their model toward reproducing important features of system behavior both qualitatively, by reproducing important visual and dynamic behaviors, and quantitatively by allowing closer comparison of patterns, relationships, and variability. 

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. 

“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. 

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. 

The ability to interpret, evaluate, and make data-based decisions is critical in the age of big data. Normative scripts around the use of data position them as a privileged epistemic form conferring authority through objectivity that can serve as a lever for effecting change. However, humans and materials shape how data are created and used which can reinscribe existing power relations in society at large (Van Wart, Lanouette & Parikh, 2020). Thus, research is needed on how learners can be supported to engage in critical data literacies through sociocultural perspectives. As a field intimately concerned with data-based reasoning, social justice, and design, the learning sciences is well-positioned to contribute to such an effort. This symposium brings together scholars to present theoretical frameworks and empirical studies on the design of learning spaces for critical data literacies. This collection supports a larger discussion around existing tensions, additional design considerations, and new methodologies.