CS021 Data Science Curriculum

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. 

The interdisciplinary field of data science, which applies techniques from computer science and statistics to address questions across domains, has enjoyed recent considerable growth and interest. This emergence also extends to undergraduate education, whereby a growing number of institutions now offer degree programs in data science. However, there is considerable variation in what the field actually entails and, by extension, differences in how undergraduate programs prepare students for data-intensive careers. The higher education theories of academic capitalism and isomorphism help elucidate what is driving differences between seminal data science curricular frameworks and offered curricula. We used two seminal frameworks for data science education to evaluate undergraduate data science programs at a subset of four-year institutions in the United States; developing and applying a rubric, we assessed how well each program met the guidelines of each of the frameworks. Most programs scored high in statistics and computer science and low in domain-specific education, ethics, and areas of communication. Moreover, the academic unit administering the degree program significantly influenced the course-load distribution of computer science and statistics/mathematics courses. Our analyses demonstrate undergraduate programs approach data science education as a largely "hard-skills-intensive," technical endeavor as academic capitalism theory predicts. Forthcoming study findings will evaluate whether current data science programs appear to demonstrate reflexive responses to isomorphic processes. Critical curricular omissions potentially create a Promethean workforce prepared to use a variety of computational and statistical tools in socially inappropriate ways.