We began our theoretical journey by thinking about the intersection of statistical literacy and critical literacy to develop a critical statistical literacy (Weiland, 2017). Since then, we have been working to take theory to practice through multiple projects while also updating our perspective to consider the emerging field of data science education. We currently think broadly about critical data literacy for critical citizenship. 

It is one thing to have a list of theoretical practices. It is another thing to consider how to support people in developing those practices. We have been working on testing some hypotheses and developing a theoretical framework for development drawing from situated cognition and communities of practice along with Paulo Freire's literacy work.

Based on our work with teachers, we have developed a set of design principles for creating meaningful learning environment for developing critical data literacies. We believe there principles can be translated to other settings to support teachers in developing critical data literacy for themselves and for teaching it to others. 

1. Data investigations of relevant issues using real data are the core of authentic practice.

2. Frame problems to investigate using dialectic tensions (i.e. difference/ representation; certain/uncertain; signal/noise) situated in issues relevant to the learners you are designing for at various levels such as international, national, community, local, home.

3. Incorporate ongoing cycles of reflection and action (i.e. praxis). 

4. Use appropriate technology tools.

5. Understanding of content and context are both valued as learning goals.

6. Community building is an explicit aspect of the design.

7. Pedagogy is modeled and made explicit. 

8. Design is transparent and explicitly communicated.