Design SDL interventions that span the full inquiry cycle, including the underemphasized cleaning and disseminating practices¶

Strategy · All strategies
Evidence · 1 claim (1 for) · 1 study (1 review), q2 · 0 of 1 report an effect size · 1 claim rests on one study

Description¶

Because the review found Cleaning Data (n = 24) and Disseminating Results (n = 19) least frequently reported among the 42 studies, it calls for instructional designs that integrate data practices "across the full cycle of scientific inquiry" rather than concentrating on analysis and visualization. Interventions should deliberately include data cleaning facets (handling missing values, filtering, removing outliers, formatting) and dissemination facets (communicating findings to broader audiences) alongside collecting and analyzing data.

Design Implications¶

Context¶

Requirements¶

• Interventions must involve students engaging directly with data as an integral part of scientific inquiry processes

Constraints¶

• Based on frequency patterns in the reviewed 2001–2023 literature; the review notes most prior interventions centered on data analysis rather than earlier inquiry practices

Target Learners¶

• K-16 science students

Target Learning Goals¶

• data cleaning and quality assurance
• communicating and disseminating scientific results

Affordances¶

• Sdl Seven Data Practices Framework

Claims¶

• Engagement in data practices is associated with both cognitive and non-cognitive learning outcomes [+W]

Related Strategies¶

Examples¶

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Key Sources¶

• Jina Kang, Chungsoo Na, Morgan Diederich, Hillary Swanson, Lili Yan. (2026). Cultivating science data literacy in K-16 science education through data practices: A systematic review. Instructional Science. https://doi.org/10.1007/s11251-026-09803-5