1. Data Literacy MARCH 12-13

2. Workshop outcomes Begin building common understanding and skills of data literacy Reflect on which data sources we use most often Reflect on which data sources should be “our main diet” Practice the four phases of a “Data Dialogue”

4. What are your data sources? Please list as many specific examples of data sources as possible that your team uses. Put each specific item on a separate post it (size = how often you use it)

5. Example Reading Conferencing Weekly Math Problem Solving DRA End-of-unit reading assess. Daily Weekly ISA 1-2 x month 2-3 x year 1x yr.

6. Types of Data Type of DataExamples Benchmark Common Assessment End-of-unit tests, common grade-level tests, common writing assess., labs Formative Classroom Assessment Student self-assessments, descriptive feedback, selected response, written response, obser. of perform., checklists, questions asked and answered in class, homework Data about people, practices, perceptions Demographics and enrollment data, surveys, interviews, observations Annual summative external assessment Aggregated, disaggregated, strand, item-level, and student work Formative common assessment Math problem-of-the-week, writing samples, science journals, other student work

7. Create a pyramid

8. Reflection Questions What do you observe? Which sources do you use the most? Which sources do you use the least? Why? What helpful sources do you not have? Waht Does this bring up any issues, questions, thoughts?

9. Not recommended pyramid Summative External Assessments Other

10. Recommended Pyramid Formative Classroom Assessment for Learning Formative Common Assessments Benchmark Common Assessments Data about people, practices, perceptions Summative External Assessments Daily, Weekly 1-4 times a month End of unit Quarterly 2-4 times per year Annually The Data Coache’s Guide Nancy Love

11. Reflection How does your pyramid compare to the “recommended” pyramid? What do you think about that?

13. Phases of Data Dialogue GO VISUAL OBSERVE The Data Coache’s Guide, Nancy Love

14. Breakfast Data Predict Observe Infer/ Question

15. Track Team Thinking Observations Inferences Questions

16. Data is not the answer “ The real value of data is not that it gives answers, but that it inspires questions.” Data Wise

17. Reflection What trends did you see? What new thoughts did you get from other teams’ thinking? What are the advantages of keeping the observation phase clearly separate? before going to inferences and questions?

19. Final Reflection What was one “aha” or “learning” you gained today regarding data and its use to improve student learning?