1. Data Use Professional Development Series 201

2. www.ride.ri.gov www.wirelessgeneration.com The contents of this slideshow were developed under a Race to the Top grant from the U.S. Department of Education. However, those contents do not necessarily represent the policy of the U.S. Department of Education, and you should not assume endorsement by the Federal Government. Rhode Island educators have permission to reproduce and share the material herein, in whole or in part, with other Rhode Island educators for educational and non-commercial purposes. © 2012 the Rhode Island Department of Education and Wireless Generation, Inc. 2

3. Welcome back! 3

4. Days 4, 5, and 6 4 Today Welcome/Overview Implementation Progress Questioning Techniques for Data Conversations BREAK Inference Validation Root Cause Analysis Effort/Impact LUNCH Data Analysis Questions Adaptive Change and Collaboration Implementation Planning BREAK Data Conversations with Parents Implementation Planning Wrap Up/Evaluations Day 5: On-Site Visit Possible activities for the Data Analysis Coach are: Collaboration time with the SDLT and/or school and district leaders. Observing Communities in Practice or Data Team meetings. Model/review Turn Key Activities. Analyze classroom data with classroom teachers. Model low stakes data conversation. Access NARS (NECAP Analysis and Reporting System) and other RIDE resources online. Day 6: Partial list of topics Longer Cycles of Inquiry and Expanding Circles of Data Use Data Conversations with Students Aggregate Data Data and Small Group Differentiation Triangulation

5. Data Use 201 Day 4 Agenda Welcome/Overview Implementation Progress Questioning Techniques for Data Conversations BREAK Inference Validation Root Cause Analysis Effort/Impact LUNCH Data Analysis Questions Adaptive Change and Collaboration Implementation Planning BREAK Data Conversations with Parents Implementation Planning Wrap Up/Evaluations 5

6. Objectives By the end of Day 4, SDLTs will be able to:  Identify challenges and successes of their data use implementation.  Engage in Data Conversations using Positive Presumptions.  Determine potential root causes of a Pattern of Need and consider effort/impact on student learning when prioritizing action.  Articulate questions appropriate to various data sources and types.  Articulate a plan for ongoing data use implementation. 6

7. Implementation Progress 7

8. 8 How many educators have you implemented with? What has surprised you the most about implementing this work at your school? What has been the biggest challenge?

9. 9

10. Data Conversations Three types of Data Conversations: Gathering Information Guiding Improvement Finding Solutions 10

11. Data Conversations Which of the three types of Data Conversations did you have most frequently? What challenges did you encounter? Gathering Information Guiding Improvement Finding Solutions 11

12. 12 Finding Solutions Is it just easier for you to teach that way because your students are more focused than mine? versus What strategies do you use to keep your students so focused? Gathering Information Is Johnny failing your class too? versus I want to learn more about Johnny’s performance in different content areas; how is he doing in your class? Guiding Improvement Are your students going to be ready for NECAP? versus What strategies are you considering to prepare your students for the NECAP? Presuming Positive Intent 12

13. Involve thinking through what you really want to know, and what assumptions you are making before you ask a question. Presume a positive result has already taken place; so you ask a question with this assumption already in mind. Presuming positive intent is not the same as “being positive.” Positive Presumptions 13

14. 14 Negative PresumptionsVersusPositive Presumptions Are you going to help Frank with that math problem? Versus Did you use quiz results to form these groups? Versus You failed this test. What happened, you didn’t study? Versus Have you developed differentiated lesson plans for your students? Versus Reframing 14

15. Implementation of the work looks different at different schools. Presuming Positive Intent makes Data Conversations more productive. Data is not an end result, but the beginning of a Conversation. Summary 15

16. 16

17. Inference Validation 17

18. Root Cause Analysis Expanding Options What else could it be? Narrowing Down Which are highly unlikely? Working Hypothesis/Data for Validating Which cause is worth further exploration? How will you know? 18

19. Root Cause Analysis 19

20. Effort/Impact 20

21. LUNCH 21

22. Data Analysis Questions

23. Adaptive Change and Collaborative Structures In general, what is the meeting topic? What questions are being asked? How can we help structure questions so that more data can be brought in to answer them?

24. Data Sources and Types What are the general questions we should be asking of all data sets? What are the questions unique to specific data sets or data types?

25. Implementation Planning 25

26. Understanding the best questions to ask of various data sources and types can help facilitate productive data meetings and Data Conversations. Summary 26

27. Data Conversations with Parents 27

28. Involve thinking through what you really want to know, and what assumptions you are making before you ask a question. Presume a positive result has already taken place; so you ask a question with this assumption already in mind. Presuming positive intent is not the same as “being positive.” Positive Presumptions 28

29. 29 Implementation Planning

30. On-Site Visit 30 Day 5: On-Site Visit Possible activities for the Data Analysis Coach are: Collaboration time with the SDLT and/or school and district leaders. Observing Communities in Practice or Data Team meetings. Model/review Turn Key Activities. Analyze classroom data with classroom teachers. Model low stakes data conversation. Access NARS (NECAP Analysis and Reporting System) and other RIDE resources online.

31. Data Walls 31 Within the context of Cycles of Inquiry As a tool for collaboration

32. Day 6 32 Some of the topics to be covered Day 6: Longer Cycles of Inquiry and Expanding Circles of Data Use Data Conversations with Students Aggregate Data Data and Small Group Differentiation Triangulation

33. Wrap Up 33