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Combined pdf of the teacher guide, slide deck, and student worksheet for the Phenology of Plants and Animals in Colorado Data Puzzles Exploration 

This mixed-methods study explores the value of the “adult learner hat” in professional learning (PL) for science teachers engaging with climate science and data sensemaking. As science education shifts towards “figuring out” versus “learning about,” teachers must learn new content, high-quality materials, and effective pedagogies. While “teacher hat” and “student hat” discursive roles have been shown to be valuable, teachers often “break” from these roles. This study focuses on the “adult learner hat,” where teachers leverage their existing knowledge to deeply engage with complex scientific concepts and data. Through pre- and post-surveys and video-based analyses of a 3-day PL workshop, we found teachers' confidence in climate science, data sensemaking, and the instructional framework significantly increased. Qualitative data revealed “adult learner hat” activities, particularly working directly with data, were identified as the primary source of this growth. We present a thematic account of PL design choices that supported this role, including surfacing teacher “teacher hat” concerns, withholding instructional documents, and purposefully redirecting teachers back to the learner role. This study highlights how the “adult learner hat” provides a productive space for teachers to grapple with uncertainty, critique visualizations, and collaborate in meaning-making, ultimately building intellectual resources for ambitious, data-rich science instruction. 

The Data Puzzles BRIDGE Research Practice Partnership builds middle school science teachers' confidence in teaching data sensemaking skills using real-world climate data. Through an ongoing teacher PLC (n=19), we seek to bring a higher degree of student epistemic agency in classrooms investigating climate phenomena. The theoretical framework of the project uniquely centers the common ground among 1) climate science education (USGCRP, 2024), 2) data sensemaking (Hunter-Thomson, 2019; Lee & Wilkerson, 2018), and 3) the Data Puzzles instructional framework (Griffith & Braaten, 2023; NRC, 2012). This project’s methods of inquiry are oriented within design-based research that centers the practices of teachers as they include Data Puzzles instructional materials in their classrooms. As part of the larger design study, we gathered teacher survey data to understand their confidence and frequency of classroom instructional practices that foreground data sensemaking (Hayes, et al., 2016) and climate literacy and education principles (e.g., USGCRP, 2024). Given that these three fields have different priorities, the central pursuit of the project is the overlapping epistemic practice (e.g., Rouse, 1996) of working with data: Students explore, analyze, interpret, and reason with data to investigate climate phenomena (Figure 1). Our design principles for building professional learning are: 1) The use of high quality, standards-aligned instructional materials is the locus for teacher learning (Harris et al., 2015; Hestness et al., 2014) that originate the laboratory work of professional interdisicplinary earth scientists 2) Teachers and students learn by working with data in supported, consequential environments, and 3) Students should work with datasets that are connected to real-world phenomena, and 4) Data sensemaking opportunities should be flexible and open-ended such that students make decisions about how to visualize and derive meaning. Results. We investigated teachers’ confidence in the three domains that intersect in this theoretical space. The initial data are teachers’ responses to a 5-point retrospective Likert item about their confidence related to data sensemaking, climate science education, and the Data Puzzles Instructional Framework before and after the professional learning. Wilcoxon’s signed-rank test was used because data were ordinal (Likert). For data sensemaking practices, confidence significantly increased from pre-test (M = 3.37, SD = 0.90) to post-test (M = 4.11, SD = 0.66) on a 5-point scale, V = 0, p < .001. Similarly, confidence in ESS-related instruction significantly increased from pre-test (M = 3.42, SD = 0.69) to post-test (M = 4.16, SD = 0.69) on a 5-point scale, V = 7, p < .001. Confidence with the Data Puzzles Instructional Model also showed a significant –and the largest–increase from pre-test (M = 1.89, SD = 0.94) to post-test (M = 4.11, SD = 0.46), V =0, p < .001. These results indicate statistically significant improvements in teacher confidence across all three interdisciplinary areas following the professional learning experience, which are likely to lead to better implementation of the Data Puzzles materials in the fall. These findings substantiate the conclusion that interdisciplinary, epistemic-practice oriented teacher learning supported by high-quality instructional materials are a promising method for an integrated, meaningful future in data science education. 

Data literacy is a critical skill for developing the next generation of scientists and informed citizens. Authentic engagement with real-world Earth science datasets in K–12 classrooms is often limited by the complexity of research data and the need for effective pedagogical strategies to support sensemaking. Data Puzzles, a collaborative effort between scientists, curriculum developers, and educators housed at the Cooperative Institute for Research in Environmental Sciences (CIRES), addresses this challenge by transforming authentic Earth and climate science datasets into structured, instructional modules designed for secondary classrooms. Each Data Puzzle is anchored in a relevant Earth system phenomenon and pairs authentic datasets (e.g., from satellite observations, field sensors, and physical samples) with research-based instructional practices from Ambitious Science Teaching (AST, Windschitl et al., 2020 ) to support data-driven reasoning, uncertainty exploration, and evidence-based explanation. Data Puzzle lessons have been adopted by hundreds of educators, empowering thousands of students to analyze and interpret authentic datasets while building critical data literacy skills. In parallel, the emerging Puzzle Piece resources allow students and teachers to extend structured learning into open-ended investigations using large, secondary datasets within a free, web-based analysis platform (CODAP). This combination of structured and open-ended resources creates an accessible pathway from curated datasets toward exploratory science practices, amplifying the’ broader impacts of science by translating complex datasets into classroom-ready learning experiences. This presentation will demonstrate how Data Puzzles serve as a model for scientists to broaden the impact of their research, enabling authentic data use and discovery in classrooms nationwide. We will share examples of how scientists’ data have been integrated into Data Puzzle lessons, highlight outcomes from teacher professional learning experiences, and discuss how this framework helps foster equitable data access and supports the next generation of Earth scientists and informed citizens. 

Teachers have expressed a desire to incorporate authentic climate data into their curricula, but struggle to find accessible and meaningful datasets that can be easily integrated into modern teaching practices. In response to this problem, climate scientists and instructional specialists from the University of Colorado Boulder have collaborated to create "Data Puzzles", a free resource that utilizes instructional practices as outlined by Ambitious Science Teaching (AST) to engage students in data analysis in the context of important scientific research. Data Puzzles challenge students to analyze and interpret climate datasets to construct explanatory models for important questions like, "What is causing the megadrought in the Colorado River Basin?” and "Why might the Arctic be warming faster than. the rest of the world?". 

Lesson materials (teacher guide, student worksheet, slide deck) associated with the "Megadrought in the Colorado River Basin" Data Puzzles lesson. 

Teacher strategies to support student data sensemaking skills