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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.
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Getting Messy with Authentic Data: Exploring the Potential of Using Data from Scientific Research to Support Student Data Literacy
Data are becoming increasingly important in science and society, and thus data literacy is a vital asset to students as they prepare for careers in and outside science, technology, engineering, and mathematics and go on to lead productive lives. In this paper, we discuss why the strongest learning experiences surrounding data literacy may arise when students are given opportunities to work with authentic data from scientific research. First, we explore the overlap between the fields of quantitative reasoning, data science, and data literacy, specifically focusing on how data literacy results from practicing quantitative reasoning and data science in the context of authentic data. Next, we identify and describe features that influence the complexity of authentic data sets (selection, curation, scope, size, and messiness) and implications for data-literacy instruction. Finally, we discuss areas for future research with the aim of identifying the impact that authentic data may have on student learning. These include defining desired learning outcomes surrounding data use in the classroom and identification of teaching best practices when using data in the classroom to develop students’ data-literacy abilities.
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- PAR ID:
- 10112628
- Date Published:
- Journal Name:
- CBE—Life Sciences Education
- Volume:
- 18
- Issue:
- 2
- ISSN:
- 1931-7913
- Page Range / eLocation ID:
- es2
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
-
Accepted Manuscript
- Journal Article:
- https://doi.org/10.1187/cbe.18-02-0023
