Reviewing the Landscape of K–12 Data Science Implementation

• In Event: Foundations of K–12 Data Science Education

Abstract

Objectives. While the literature base on data science learning is still being established, several school districts and state departments of education have already initiated programmatic work to offer data science content, courses, and pathways to students on a broad scale. This paper explores those ongoing efforts, with a focus on describing, exemplifying, and assessing the benefits and challenges of different implementation models. We explore the structural and pedagogical dimensions of these models in light of major frameworks and standards documents that are shaping the adoption of data science at the K-12 level.

Perspective. Our analysis is sensitive to the complexity and multiple constraints present in current school systems. The success of curriculum implementation and reform efforts depend on a number of factors including resources available, statewide and parental support, educator support and training, student readiness and coordination with existing curriculum structures, and more. Thus, we attend to specific policy, state, district, and school leadership structures both as they exist “in theory” (through document analysis of policy and initiatives) and “in practice” (as reported through interviews with stakeholders and emerging empirical work).

Mode of inquiry. We focus our implementation models analyses and education leader interviews on specific state (e.g., Oregon, Georgia, California, Ohio, Arkansas, Hawaii, Nebraska) and district-level initiatives (e.g., San Diego Unified School District, Los Angeles Unified School District, Khan Lab School, New York - KIPP), with attention to geographic and student-demographic diversity in examples.

Evidence. Our analysis offers a comparative and synthetic review of implementation models drawing from several related data sources including: (1) a field scan of influential international, national, and professional policy documents and working group outputs; (2) emerging implementation models that are being adopted by state, district, and independent school system pilot initiatives (e.g., NGSS; GAISE II; International Data Science in Schools Project); (3) in-depth case studies (Yin, 2009) that highlight student and educator experiences and implementation needs and challenges within this models; (4) interviews with educational leaders spearheading implementation efforts; and (5) a review of extant literature highlighting the pros and cons of different implementation models.

Results. The analysis identifies at least 6 distinct implementation models for extending data science offerings to K-12 students in formal contents. These include data science courses offered as mathematics pathways; elective courses; integration into other subjects (i.e., science, social studies); ‘modernization’ of existing mathematics offerings; or as career & technical education options. These demonstrate the diversity of offerings and implementation models state to state, and interviews shed light on the necessary conditions for reform within various models.

Significance. Although there is considerable basic research into the learning of data among students, as well as research into specific programs, less is known about the broader landscape of data science education. Understanding the current state of “on the ground” implementation efforts presents the need and opportunity for more comparative and large-scale research into the relative needs, benefits, and drawbacks of distinct models.

Authors

• Zarek Drozda, University of Chicago
  Presenting Author

• Davis Johnstone
  Non-Presenting Author

• Brooke Van Horne
  Non-Presenting Author

• Ava Severts
  Non-Presenting Author