Data Teams at Windham Middle School in the context of THE seed pilot

1. Data Teams at Windham Middle Schoolin the context of THE seed pilot Presented by Jane Cook Adapted from materials developed by the Leadership and Learning Center

2. What Are Data Teams? • Small grade-level, interdisciplinary or vertical content area teams that examine individual student work generated from standardized and non-standardized Indicators of Academic Growth and Development (IAGDs) • Collaborative, structured, scheduled meetings that focus on the effectiveness of teaching and learning Data Teams

3. Data Team Actions “Data Teams adhere to continuous improvement cycles, examine patterns and trends, and establish specific timelines, roles, and responsibilities to facilitate analysis that results in action.” (S. White, Beyond the Numbers, 2005, p. 18) Data Teams

4. Illustration of Core Requirements of SEED Teacher Evaluation PILOT Student Growth and Development (45%) Whole-school Student Learning Indicators or Student Feedback (5%) Observations of Performance and Practice (40%) Peer or Parent Feedback (10%) Observations & Surveys Student Learning Objectives (SLOs) Practice Rating (based on Cause Data) (50%) Outcome Rating (based on Effects Data) (50%) All factors are combined to reach each teacher’s final annual rating (as described in the Connecticut guidelines).

5. The Data Team Process • Step 1—Collect and chart data AKA “The Treasure Hunt” • Step 2—Analyze strengths and obstacles • Step 3—Establish SMART goals that are Specific, Measurable, Achievable, Relevant/Realistic, and Timely: Set, Review, Revise • Step 4—Select instructional strategies: What effective teaching strategies will adults use to help students achieve SMART goals? • Step 5—Determine results indicators: What measures will we use? How will we know that we have succeeded? Data Teams

6. HOW The data Team process aligns with setting Student learning objectives IN SEED

7. Do Data Teams Really Work? One district’s story: • 80% free and reduced lunch • 68% minority student enrollment • 40+ languages (D. Reeves, The Learning Leader, 2006) Data Teams

8. One district’s story:7 YEARS OF PROGRESS FROM 1998 to 2005 D. Reeves, The Learning Leader, 2006

9. Asking the Right Questions • What does student achievement look like (in reading, math, science, writing, foreign language, tech ed, music, art, physical education, health)? • What variables that affect student achievement are within our control? • How do we currently explain our results in student achievement? Data Teams

10. Data Worth Collecting: Have a Purpose • How do we use data to inform instruction and improve student achievement? • How do we determine which data are the most important to use, analyze, or review? • In the absence of data, what is used as a basis for instructional decisions? Data Teams

11. Two Types of Data “In the context of schools, the essence of holistic accountability is that we must consider not only the effect variable—test scores—but also the cause variables—the indicators in teaching, curriculum, parental involvement, leadership decisions, and a host of other factors that influence student achievement.” (D. Reeves, Accountability for Learning, 2004) Data Teams

12. Two Types of Data • Effect Data: Student achievement results from various measurements, both standardized and non-standardized – Related to SEED Outcome Rating • Cause Data:Information based on actions of the adults in the system – Related to SEED Practice Rating Data Teams

13. Effect Data(AKA STUDENT ACHIEVEMENT DATA) How does this effect data answer your questions about student achievement? What types of effect data are you collecting and using? What other data do you need to analyze? Data Teams

14. Cause Data (aka Adult Actions) What types of cause data are you collecting? How do you use this cause data to change instructional strategies? How does this cause data support your school or team goals and focus? Data Teams

15. Data Should Invite Action “Data that is collected should be analyzed and used to make improvements (or analyzed to affirm current practices and stay the course).” (S. White, Beyond the Numbers, 2005, p. 13) If the data that you are collecting and analyzing is not helping inform your practice, i.e., planning, curriculum, instruction, or assessment, use different data. - Jane Cook, WMS Data Team Training Data Teams

16. Effects/Results Data The Leadership/Learning Matrix (L2 Matrix) Antecedents – Adult Actions/Interventions Cause Data Data Teams

17. “Effective analysis of data is a treasure hunt in which leaders and teachers find those professional practices—frequently unrecognized and buried amidst the test data—that can hold the keys to improved performance in the future.” (D. Reeves, The Leader’s Guide to Standards, 2002) Data-Driven Decision Making Data Teams

18. Collaborate Communicate expectations Form Data Teams Identify Data Team facilitators Schedule meetings Data Team meetings Principal and Data Team facilitators Post data and graphs Create communication system Steps to Create and Sustain Data Teams Data Teams

19. Collaborative teams Plan, Do, Study, Act (PDCA) Total Quality cycle Shared beliefs about student achievement Continuous improvement Shared inquiry Commitment to results Effective Collaboration Effective Collaboration Data Teams

20. What Is Needed for Effective Data Teams? • Effect data (student achievement) and cause data (adult actions) • Authority to use the data for instructional and curricular decisions • Supportive, involved building administrators • Positive attitude Data Teams

21. Collaboration: The Heart of Data-Driven Decision Making • What is collaboration? • What does collaboration look like? • How do you start collaborating? • How do you create a self-sustaining capacity for a collaborative culture? Data Teams

22. Communicating Expectations • Do we indeed believe that all kids can learn? • What does this belief look like in our school? • How do we know that all students are learning? • What changes do we need to make to align practices with beliefs? Data Teams

23. DATA TEAM CONFIGURATIONS - Vertical Data TeamS Data Teams

24. DATA TEAM CONFIGURATIONS – Horizontal Middle school Data Team Data Teams

25. DATA TEAM CONFIGURATIONS - SpecialS teachers Data Team Data Teams

26. Team Member Responsibilities Come prepared to meeting Assume a role Participate honestly, Respectfully, constructively Be punctual Engage fully In the process Data Teams

27. Roles of Data Team Members Data Teams

28. Data Team Leaders • Are not expected to: • Serve as pseudo-administrators • Shoulder the responsibilities of the whole team • Address peers and colleagues who do not want to cooperate • Evaluate colleagues’ performance Data Teams

29. Data Team Leaders • Reflect on the needs of the staff and/or their team • Work collaboratively to overcome obstacles Data Teams

30. Data Team Leader and Principal Debriefs • Meet at least monthly to discuss • Achievement gaps • Successes and challenges • Progress monitoring • Assessment schedules • Intervention needs • Resources • Team needs Data Teams

31. Lessons from the geese Fact lesson People who share a common direction and sense of community can get where they are going quicker and easier, because they are traveling on the thrust of each other. • 1: As each goose flaps its wings, it creates an “uplift” for the birds that follow.   By flying in a “V” formation, the whole flock has 71% greater flying range than if each bird flew alone. Source: http://www.leadershipi2i.com/geese.cfm

32. Lessons from the geese Fact lesson If we have as much sense as a goose, we stay in formation with those headed where we want to go.   We are willing to accept their help and give our help to others. • 2: When a goose falls out of formation, it suddenly feels the drag and resistance of flying alone.   It quickly moves back into formation to take advantage of the lifting power of the bird immediately in front of it. Source: http://www.leadershipi2i.com/geese.cfm

33. Lessons from the geese Fact lesson It pays to take turns doing the hard tasks and sharing leadership.   As with geese, people are interdependent on each others’ skills, capabilities, and unique arrangement of gifts, talents, or resources. • 3: When the lead bird tires, it rotates back into the formation to take advantage of the lifting power of the bird immediately in front of it. Source: http://www.leadershipi2i.com/geese.cfm

34. Lessons from the geese Fact lesson We need to make sure our honking is encouraging.   In groups where there is encouragement, the production is much greater.   The power of encouragement (to stand by one’s heart or core values and to encourage the heart and core values of others) is the quality of honking we seek. • 4: The geese flying in formation honk to encourage those up front to keep up their speed. Source: http://www.leadershipi2i.com/geese.cfm

35. Lessons from the geese Fact lesson If we have as much sense as geese, we will stand by each other in difficult times as well as when we’re strong. • 5: When a goose gets sick, wounded, or shot down, two geese drop out of formation and follow it down to help and protect it.   They stay with it until it dies or is able to fly again.   Then, they launch out with another formation to catch up with the flock. Source: http://www.leadershipi2i.com/geese.cfm

36. Burning Questions