Cultivating science data literacy in K-16 science education through data practices
A systematic review
Bibliographic Data
| ID | 21335938 |
|---|---|
| Authors | Jina Kang (0000-0002-7043-5427, University of Illinois Urbana-Champaign, corresponding author), Chungsoo (0000-0003-3411-368X, Utah State University), Morgan Diederich (0000-0001-6895-8280, Utah State University), Hillary Swanson (0000-0001-5953-6780, Utah State University), Lili Yan (0000-0003-4491-1853, Michigan State University) |
| Year | 2026 |
| Volume | 54 |
| Issue | 2 |
| Publication date | 2026-09-01 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Instructional Science (JOURNAL) |
| Journal identifiers | ISSN: 0020-4277 • E-ISSN: 1573-1952 |
| Publisher | Springer Science and Business Media LLC (PUBLISHER) |
| DOI | 10.1007/s11251-026-09803-5 |
| OpenAlex | W7162865560 |
| Language | EN |
| References cited | 83 |
As data science advances with the emergence of new computational technologies, science data literacy (SDL)—the ability to understand, use, and critically engage with data to address real-world scientific problems—becomes increasingly vital in science education. However, research on how data practices vary across scientific disciplines and educational levels remains limited, hindering the development of a more cohesive understanding of how SDL can be systematically integrated into diverse educational contexts. This review examines SDL by analyzing 42 peer-reviewed empirical studies (2000–2023) to investigate how students have been engaged in data practices across K–16 science education research. We identify seven core data practices: understanding problems, designing experiments, collecting data, cleaning data, analyzing data, and evaluating and disseminating results, along with their associated facets. Through frequency and association analyses, we observe systematic differences in how these practices are emphasized across disciplines, grade levels, and data types. The reviewed studies suggest that engagement in data practices is associated with both cognitive and non-cognitive learning outcomes. Building on established characterizations of scientific inquiry and problem-solving, along with frameworks such as NGSS and GAISE, we conceptualize SDL as a multifaceted and iterative process, encompassing dimensions from problem identification to data analysis, result synthesis, and dissemination. These dimensions illuminate how scientific knowledge is developed, critiqued, and communicated, which is critical for STEM workforce preparation. Lastly, we identify key challenges evident in the literature and outline directions for future research to support interdisciplinary and sustained approaches to SDL
Best practice · Discipline · Educational psychology · Educational research · Identification (biology) · Literacy · Science education · Scientific Literacy · Workforce development · Educational Assessment and Improvement · Research Data Management Practices · Statistics Education and Methodologies
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| Citation velocity | historical |
|---|---|
| Highly cited | No |