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ESU 4 Data Retreat: Engaging in Data-Driven Dialogue

ESU 4 Data Retreat: Engaging in Data-Driven Dialogue. Mitzi Hoback, Ellen Stokebrand , & Suzanne Whisler NDE Data Conference, April 2012. Data Retreat at ESU 4. It’s all about improving learning for all students. .

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ESU 4 Data Retreat: Engaging in Data-Driven Dialogue

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  1. ESU 4 Data Retreat: Engaging in Data-Driven Dialogue Mitzi Hoback, Ellen Stokebrand, & Suzanne Whisler NDE Data Conference, April 2012

  2. Data Retreat at ESU 4

  3. It’s all about improving learning for all students. How do we know if what we are doing is making a difference for all students?

  4. Data Retreat Outcomes

  5. Big Ideas of Data Retreat • Teachers and leaders matter! • It is what teachers and leaders do that matters the most. • Data should provide a starting point and focus for your actions, help assess your progress, and identify where you are being successful and where there is a need for more support. • Use data in an ongoing way.

  6. Pledge of Confidentiality

  7. “Conversations are soft on people, hard on ideas”

  8. As you move through Data Retreat… Start Your “To Do” List This will help you complete your Roll-Out Plan at the end of Day 2

  9. Continuous Improvement Process What is your current reality? What does your data tell you? How will you implement your plan and monitor results? What goal will you set based upon current reality? What actions need to be taken to meet the goal? What is your school’s mission statement?

  10. What question(s) do you plan to answer during this Data Retreat? Some examples: • To what extent have specific programs, services, or interventions improved outcomes? • Which students have been on our “At Risk” list for more than one year and what progress has been made? • How does absence affect assessment results? • Do we see correlations in our data? • Can we predict how our students will do on future assessments? (NeSA Tests) • What should be celebrated? *ILCD*

  11. If the data is not being used, stop collecting it.

  12. Student Data Analysis Learnings Other Data Do we have other data to support these results? What can we learn from this data? Implications Actions What are the implications of this data? What will we do as a result of the implications?

  13. Observations Strengths Challenges Hypotheses

  14. Data Sources • Norm-Referenced Tests (NRT’s) • NeSA • Reading • Writing • Math • Science • DIBELS • PLAN • ACT

  15. A Vision For Results “The vision should be “We’re going to get every kid over that bar.” ~Rick DuFour

  16. IndividualStudentData What evidence do you have that ALL students are learning?

  17. Identify Your At-Risk Students

  18. Are there students not meeting your district’s expectations? • Who are they? • How does this list compare to last year’s list? New students? Same students? • What is your hypothesis regarding why these students aren’t meeting expectations? • If the same students are on the list, what data do you have about the interventions you tried?

  19. Celebrations • How will you celebration milestones and successes? • Celebrate early wins! • Celebrate often! (Quarterly)

  20. Designing the Roll-Out Plan • Team Task – Discuss & determine … • How will help the staff take ownership of the plan? • How will we engage the rest of the staff in the data? • How will we engage the staff in the discussions, observations, hypotheses and ideas? • How will we engage them in the goals? • How will we engage them in the improvement tasks and culture of improvement?

  21. Roll Out and Sustainability How will you roll out the action plan to the rest of the staff?

  22. Sharing the Data • “When we give teachers the opportunity to see even gradual improvements through the use of data, they can become passionate advocates of the system.” ~Schmoker When and how will your team share the data with your colleagues?

  23. Final Thoughts . . . “Teamwork is the ability to work together toward a common vision.  The ability to direct individual accomplishment toward organizational objectives.  It is the fuel that allows common people to attain uncommon results. ” ~Andrew Carnegie

  24. “Commit to data analysis as a continuous process, not an event.” Douglas Reeves, 2009

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