Checklist for Building Data Science Education

The Department's model for the implementation—and continuous improvement—of evidence-based interventions, including those related to data education and data literacy, includes five steps.

The following checklist summarizes the key components of each step.

1. Identify Local Needs
• Consult existing learning-based requirements and student needs
• Meaningfully engage local stakeholders
• Seek systematic alignment with post-graduation pathways
• Consider piloting first, and design for iterative goals
• Determine outcomes that matter most to your context and locality
2. Select Relevant, Evidence-Based Interventions
• Select programs that demonstrate a clear rationale and support infrastructure
• Select programs that reflect components of evidence-based practices
• Explore prior data science programs and outcomes from peer schools and districts
3. Plan for Implementation
• Build a logic model for your approach
• Develop detailed implementation plans, timelines and workplans
• Ensure sufficient time and resources for educator training
• Ensure sufficient resources for acquiring hardware and software
• Ensure sufficient time for student and family engagement
• Develop plans for continuous improvement, data collection and evaluation
4. Implement
• Implement your plans
• Collect data
5. Examine and Reflect
• Review data and refine your approach
• Begin continuous improvement cycle again as appropriate