1. Data Teams

2. Data Teams in Action Medical/Education connections Double Entry Journal MVP – Most Valuable Point Explain one MVP with a partner and then as a group

3. Looking into the Future Your role as Leaders in a data-driven school The teacher’s role in a data-driven school The student’s role in a data-driven school The parent’s role in a data-driven school

4. Data Teams are a model for continuous, collaborative action that inspires and empowers professionals to improve teaching, learning, and leadership for all.

5. Data Teams have a common focus or common standard, a common formative assessment, and a common scoring guide. 4

6. Data Teams are small, grade-level, department, course-alike, or organizational teams that examine work generated from a common formative assessment.

7. Data Teams meetings are collaborative, structured, scheduled meetings that focus on the effectiveness of teaching and learning.

8. One Minute Assessment #1 Reflect on the three definitions of data teams. Which component of data teams do you think will stretch you the most as a leader? Which do you think will stretch your teachers?

9. 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, 2005

10. ` We are a Professional Learning Community. We do Data Teams.

11. Four Critical Questions that Guide a PLC 1. What are students supposed to know and be able to do? 2. How do we know when our students have learned? 3. How de we respond when students haven't learned? 4. How do we respond when students already know the content? PRIORITY STANDARDS COMMON FORMATIVE ASSESSMENTS INTERVENTION/DIFFERENTIATION EXTENSION/DIFFERENTIATION

12. Learning Targets I can select relevant data to track progress toward increased achievement I can interpret data to reveal strengths and targeted areas for growth I can relate the impact of adult actions to the learning of students I can actively participate to keep our learning moving forward ???

13. The Data Teams Process 8-9

14. Inquiry and Treasure Hunt Analyze to prioritize Set SMART goals Select strategies Determine results indicators Monitor and evaluate results Decision- Making for Results

15. Data Team Testimonials 12

17. One Minute Assessment #2 Construct a metaphor or an analogy that connects standards, assessments, results, and instruction.

18. Developing a Data Mind-Set 15

19. Data Teams focus only on Priority Standards.

20. Readiness Endurance Leverage Priority Standards 17

21. “Unwrapping” allows you to get to what’s most important. First priority reading standard on p. 18

22. Unwrapping Standards Understand and apply knowledge of the complementary nature of structure and function and the commonalities among organisms. Utilize interactive literacy and social skills to establish personal, family, and community health goals.

23. Assessments

24. A brief review of key ideas…

25. 25 Benefits of Formative AssessmentsClarifyLearningIntentions Elicit Evidence of Learning PromoteFeedback Encourage Learners to Own Their Learning

26. Formative What students should know How instruction needs to be adjusted How to improve student skills Assessment FOR learning

27. Summative After instruction occurs Used when determining grades Various audiences for the data Assessment OF learning

28. Remember… It isn’t the method that determines whether the assessment is summative or formative, it is how the results are used.

29. Pick the right tool for the right job…

30. “Schools with the greatest improvements in student achievement consistently used common assessments.” D. Reeves, 2004

31. Why match a priority learning target with an assessment method?

32. Assessment Types 21

33. Remember… Purposeful assessments not only measure the impact but also the innovation that cause change in student learning. Effective assessment procedures and use of the associated data are fundamental to a school’s continuing achievement and improvement.

34. Two Types of Data Effect Data: Student achievement results from various measurements Cause Data: Information based on actions of the adults in the system

35. What content area is our highest priority? Which sub-group of students are most in need of improvement? In what sub- content/skill area(s) do most students need to improve? What skill set needs additional support or intervention? Effect Data: % of ELL students Effect Data: % of 8th grade students scoring proficient or advanced on the state reading assessment Effect Data: % of ELL students who will likely be proficient in literary response Effect Data Source: % of students who will need extensive support in literary response Drilling Down with Effect Data 32-33

36. What content area is our highest priority? Which sub-group of students are most in need of improvement? In what sub- content/skill area(s) do most students need to improve? What skill set needs additional support or intervention? District/school response: Activity: Drilling Down with Effect Data District/school response:

37. What content area is our highest priority? Which sub-group of students are most in need of improvement? In what sub- content/skill area(s) do most students need to improve? What skill set needs additional support or intervention? Adult action: Number of meetings held for the purpose of making student achievement decisions with relevant, timely data. Adult action: Percentage of instructional time focused on the high-priority content area. Adult action: Frequency of team meetings focused on differentiated learning/instructional groups for specific concepts and skills. Adult action: Number of effective teaching strategies implemented to support specific concepts and skills. Drilling Down with Cause Data 34-35

38. What content area is our highest priority? Which sub-group of students are most in need of improvement? In what sub- content/skill area(s) do most students need to improve? What skill set needs additional support or intervention? Adult action: Activity: Drilling Down with Cause Data

39. Application of Antecedents of Excellence Results Indicators High results, high understanding Replication of success likely Losing Low results, low understanding Replication of mistakes likely Learning Low results, high understanding Replication of mistakes unlikely High results, low understanding Replication of success unlikely Leadership and Learning Matrix LeadingLucky Cause Data Effect Data

40. Part Three The Data Team Meeting

41. Now, experience the Data Team process… 107

42. Step 1 - Collect and Chart data Review the data team rubric on p. 109 Evaluate the team (based on the rubric) with regard to how data was collected and charted DMPS application – role of technology in collecting and charting data

43. Check for Understanding 1.Process followed by a Leadership Team 2.Process requiring collaboration 3.Begins with a few driving questions about achievement in the school 4.Examines the impact of teacher actions on student learning 5.Can be an agenda item during part of a longer meeting 6.Replaces PLCs in your building 7.Requires the monitoring of broad/varied set of data points 8.Requires participants to go out and find relevant data 9.Requires participants to examine data from a specific student task DMRDATA TEAMSBOTHNEITHER

44. Step 2 – Analyze/Prioritize Review the data team rubric on p. 114 Evaluate the team (based on the rubric) with regard to how data was analyzed and prioritized DMPS application – realistic challenges with analysis and assessment design

45. Step 3 – Set Goals Review the data team rubric on p. 117 Evaluate the team (based on the rubric) with regard to how goals were set DMPS application – goal setting and student motivation

46. Step 4 – Select Strategies Review the data team rubric on p. 122 Evaluate the team (based on the rubric) with regard to how strategies were selected DMPS application – resources and support structure

47. Step 5 – Results Indicators Review the data team rubric on p. 127 Evaluate the team (based on the rubric) with regard to how results indicators were established DMPS application – “support” and “pressure” in equal doses

48. Step 6 – Monitor/Eval. Results Review the data team rubric on p. 130 Evaluate the team (based on the rubric) with regard to how results indicators were established DMPS application – “support” and “pressure” in equal doses

49. Culminating Evaluation Data Team functioning as a PLC Use rubrics from pages 132-135 to evaluate the degree to which these educators function as a team.

50. Weed the garden!

51. One Minute Assessment #3 What tools do your teachers have to do the Data Teams job right now?

52. Some challenges Independent Contractor The Crow Eddie Haskell The Magpie The Multi-Tasker The Pendulum The Deflater The Procrastinator The Collector (admin)

53. Some solutions??? Independent Contractor The Crow Eddie Haskell The Magpie The Multi-Tasker The Pendulum The Deflater The Procrastinator The Collector (admin)

54. Synthesis Statement Consider your three responses to the 1- minute assessments Create a synthesis statement that ties these three observations into a unique, personal observation on the use of data and the data teams process Share around your table

55. Implementation What it means to be “doing data teams”

56. School Culture and Leadership Transparency Inspiration Pay attention to cause and effect data Be data-informed yourself Learn this by doing this

57. Cause Factors Effects High results, high understanding Replication of success likely Losing Low results, low understanding Replication of mistakes likely Learning Low results, high understanding Replication of mistakes unlikely High results, low understanding Replication of success unlikely Leadership and Learning Matrix LeadingLucky

58. Learning Targets I can select relevant data to track progress toward increased achievement I can interpret data to reveal strengths and targeted areas for growth I can relate the impact of adult actions to the learning of students I can actively participate to keep our learning moving forward ???

59. Decision Making for Results Data Teams Des Moines Public Schools August 1-2, 2011