Summary
This project, funded by NSF, aims to develop and implement practical and scalable K-12 data science pathways in four school districts, integrating data science with computational thinking and core academic subjects.
What they want
The project is a research-practice partnership between Digital Promise, Looking Glass Ventures, Data Science 4 Everyone, and four school districts (Broward County, FL; Indian Prairie, IL; Iowa City, IA; Talladega County, AL). It supports districts in developing practical and scalable pathways for teaching data science from early elementary through high school, designing and implementing instructional progressions that integrate data science with computational thinking and core academic subjects. The work involves adapting each district’s specific existing computational pathways while maintaining a common trajectory and key data science concepts. The project will contribute new knowledge about how districts can design and scale data science pathways, examining how district leaders adapt and extend existing computational thinking pathways to incorporate data science and machine learning. Research will focus on understanding conditions that support integration, including district-level planning structures, instructional tools, and cross-district collaboration. Using a mixed-methods approach, the study will analyze implementation artifacts, district leader and teacher feedback, and course data to identify scalable practices and assess their impact on instructional coherence and district-level capacity. By comparing implementation approaches across four districts, the project aims to advance models for sustainable, peer-driven expansion of integrated computing and data science education.
Deliverables
- Practical and scalable pathways for teaching data science from early elementary through high school
- Instructional progressions that integrate data science with computational thinking and core academic subjects
- Adapted district-specific existing computational pathways incorporating data science and machine learning
- New knowledge about how districts can design and scale data science pathways
- Analysis of implementation artifacts, district leader and teacher feedback, and course data
- Identification of scalable practices for data science integration
- Assessment of impact on instructional coherence and district-level capacity
- Models for sustainable, peer-driven expansion of integrated computing and data science education
Technical requirements
- Integration of data science with computational thinking and core academic subjects
- Focus on artificial intelligence and machine learning foundations