Previewing the National Landscape on K-12 Data Science Implementation

Data is undeniably changing our world. How is K-12 education responding? This paper previews the national landscape of K-12 data science implementation through a field review of existing frameworks and policy, analysis of case studies on various implementation models, and in-depth stakeholder interviews. We find that state standards across subjects are framing data science courses through multiple implementation models while curriculum providers are creating additional content. We also highlight areas of further investment to capture emerging opportunities in data science education.
Content frameworks across multiple school subjects in primary and secondary education already incorporate some learning about data. We analyze eight frameworks, including the ASA-NCTM GAISE II Report, and highlight similarities and differences across the frameworks. Efforts to create explicit learning opportunities in data science have been articulated at the state-level in 14 states, with the majority of data science standards implemented through mathematics. Data science and data practices have also been articulated through science and computer science standards.
In our review of models for implementing data science into K-12 education, we find data science opportunities most frequently articulated as high school mathematics offerings or as Career and Technical Education (CTE) sequences. Meanwhile, integration into science, social studies, and other subjects is too often "brushed over." Many stakeholders also expressed a need to foster data literacy beginning in elementary and middle school grades as universal content. A small but growing number of out-of-school programs engage students by using relevant, real-word data and utilizing competition models and project-based learning.
Stakeholder interviews revealed that curricula in data science have been uniquely engaging for students, yet robust professional development continues to be needed to build teacher confidence.

Data science education

Statistics education

Data literacy

K-12 data learning

GAISE II

Data science and statistics education 

Beginner

Education

Symposium on Data Science and Statistics (SDSS) 2023