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Data Teams

Data Teams. 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. Looking into the Future . Your role as Leaders in a data-driven school The teacher’s role in a data-driven school

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Data Teams

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

  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. 8-9 The Data Teams Process

  14. Decision-Making for Results

  15. 12 Data TeamTestimonials

  16. Part Two Building the Foundation:Standards, Assessment, Data

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

  18. 15 Developing a Data Mind-Set

  19. Data Teams focus only on Priority Standards.

  20. Priority Standards Readiness Endurance Leverage 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. Benefits of Formative Assessments

  26. Assessment FOR learning

  27. 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. 21 Assessment Types

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

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

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

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

  39. Leadership and Learning Matrix Lucky Leading Effect Data Results Indicators High results, low understanding Replication of success unlikely 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 Application of Antecedents of Excellence Cause Data

  40. Part Three The Data Team Meeting

  41. 107 Now, experience the Data Team process…

  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 • Process followed by a Leadership Team • Process requiring collaboration • Begins with a few driving questions about achievement in the school • Examines the impact of teacher actions on student learning • Can be an agenda item during part of a longer meeting • Replaces PLCs in your building • Requires the monitoring of broad/varied set of data points • Requires participants to go out and find relevant data • Requires participants to examine data from a specific student task DMR DATA TEAMS BOTH NEITHER

  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!

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