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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.
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This content will become publicly available on April 8, 2027
Finding the Sweet Spot Spot in Interdisciplinary Data Science Education: The Data Puzzles Model (Poster 8)
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.
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- Award ID(s):
- 2405911
- PAR ID:
- 10703270
- Publisher / Repository:
- American Educational Research Association
- Date Published:
- Format(s):
- Medium: X
- Location:
- Los Angeles
- Sponsoring Org:
- National Science Foundation
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