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School Counselors Data-Based Decision-Making

School Counselors Data-Based Decision-Making . Washington School Counselors Association John Carey National Center for School Counseling Outcome Research UMass Amherst www.cscor.org. Seven Steps in Using Data in Advocacy and Systems Change. 1. Describe the Problem. 2. Generate Vision

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School Counselors Data-Based Decision-Making

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  1. School Counselors Data-Based Decision-Making Washington School Counselors Association John Carey National Center for School Counseling Outcome Research UMass Amherst www.cscor.org

  2. Seven Steps in Using Data in Advocacy and Systems Change 1. Describe the Problem 2. Generate Vision Data 3. Commit To Benchmarks 4. Identify Places to Intervene: First Order Change? Second Order Change? 6. Evaluate Implementation 5. Select Interventions 7. Monitor Problem Data

  3. Seven Steps in Using Data in Advocacy and Systems Change 2. Generate Vision Data 3. Commit To Benchmarks 1. Describe the Problem 4. Identify Places to Intervene: First Order Change? Second Order Change? 6. Evaluate Implementation 5. Select Interventions 7. Monitor Problem Data

  4. Seven Steps in Using Data in Advocacy and Systems Change 1. Describe the Problem 2. Generate Vision Data 3. Commit To Benchmarks 4. Identify Places to Intervene: First Order Change? Second Order Change? 6. Evaluate Implementation 5. Select Interventions 7. Monitor Problem Data

  5. Seven Steps in Using Data in Advocacy and Systems Change 1. Describe the Problem 2. Generate Vision Data 3. Commit To Benchmarks 4. Identify Places to Intervene: First Order Change? Second Order Change? 6. Evaluate Implementation 5. Select Interventions 7. Monitor Problem Data

  6. Seven Steps in Using Data in Advocacy and Systems Change 1. Describe the Problem 2. Generate Vision Data 3. Commit To Benchmarks 4. Identify Places to Intervene: First Order Change? Second Order Change? 6. Evaluate Implementation 5. Select Interventions 7. Monitor Problem Data

  7. Seven Steps in Using Data in Advocacy and Systems Change 1. Describe the Problem 2. Generate Vision Data 3. Commit To Benchmarks 4. Identify Places to Intervene: First Order Change? Second Order Change? 6. Evaluate Implementation 5. Select Interventions 7. Monitor Problem Data

  8. Seven Steps in Using Data in Advocacy and Systems Change 1. Describe the Problem 2. Generate Vision Data 3. Commit To Benchmarks 4. Identify Places to Intervene: First Order Change? Second Order Change? 6. Evaluate Implementation 5. Select Interventions 7. Monitor Problem Data

  9. Seven Steps in Using Data in Advocacy and Systems Change 1. Describe the Problem 2. Generate Vision Data 3. Commit To Benchmarks 4. Identify Places to Intervene: First Order Change? Second Order Change? 6. Evaluate Implementation 5. Select Interventions 7. Monitor Problem Data

  10. Seven Steps in Using Data in Advocacy and Systems Change 2. Generate Vision Data 3. Commit To Benchmarks 1. Describe the Problem 4. Identify Places to Intervene: First Order Change? Second Order Change? 6. Evaluate Implementation 5. Select Interventions 7. Monitor Problem Data

  11. Step 1: Describe the Problem • Disaggregation Tools • Triangulation Tools

  12. Disaggregating Data • Comparing and contrasting the performance of different groups of students on some outcome measure. (Results Data) • Comparing and contrasting the rates of participation of different groups of students in school programs and activities related to outcomes. (Process Data) • Comparing and contrasting the perceptions of different groups of students on factors related to outcomes. (Perceptual Data)

  13. Disaggregation Categories • Race • Gender • Limited English Proficient • Academic/Vocational Track • English Language Learners • Free or Reduced School Lunch • Mobility • Special Needs • Achievement Quartile • Grade

  14. Disaggregating DataAchievement Outcomes2002 10th Grade MCAS English Language Arts

  15. Disaggregating DataAchievement Outcomes2002 10th Grade MCAS English Language Arts

  16. Disaggregating DataAchievement Outcomes2002 10th Grade MCAS English Language Arts

  17. Disaggregating DataAchievement Outcomes2002 10th Grade MCAS Mathematics

  18. Disaggregating DataAchievement Outcomes2002 10th Grade MCAS Mathematics

  19. Disaggregating DataAchievement Outcomes2002 10th Grade MCAS Mathematics

  20. Disaggregating School Process Data% Attending Gateway ClassesPartnership District Data

  21. Disaggregating Perceptual DataSchool Climate SurveyMinority Student Achievement Project I work hard in school because the teacher demands it.

  22. Disaggregating Perceptual DataSchool Climate SurveyMinority Student Achievement Project I am happy to be at this school.

  23. Disaggregating Perceptual DataSchool Climate SurveyMinority Student Achievement Project How many teachers know how capable you are to do well in school?

  24. Triangulate • Use three different independent sources of data to describe the problem. • Results Data • School Process Data • Perceptual Data

  25. Triangulating Results Data School Process Data THE PROBLEM Perceptual Data

  26. Triangulating High percentages of African American Students Fail 10th grade MCAS. School Process Data THE PROBLEM Perceptual Data

  27. Triangulating High percentages of African American Students Fail 10th grade MCAS. Low percentages of African American Students Take 8th Grade Algebra and Algebra 2 THE PROBLEM Perceptual Data

  28. Triangulating High percentages of African American Students Fail 10th grade MCAS. Low percentages of African American Students Take 8th Grade Algebra and Algebra 2 THE PROBLEM Many African American Students Report Teachers Do Not Think They Are Able To Go To College.

  29. Seven Steps in Using Data in Advocacy and Systems Change 1. Describe the Problem 2. Generate Vision Data 3. Commit To Benchmarks 4. Identify Places to Intervene: First Order Change? Second Order Change? 6. Evaluate Implementation 5. Select Interventions 7. Monitor Problem Data

  30. Step 2: Generate Vision Data • Successful Vision Data is: • Agreed upon by most members of the system • Concrete and specific • Measurable • Related to student learning outcomes • Ambitious • Attainable • Tied to a deadline • Related to Values and Passion

  31. Generate Vision Data: How to do it • Start with a description of the status quo • What pleases you about the students’ current achievement data? • What disturbs you about your students’ current achievement data? • Generate a quantitative description of achievement data some specific time in the future. • What should the data look like in five years?

  32. Vision Data: MCAS Pass Rates for History and Social Studies in 2003

  33. Vision Data: MCAS Pass Rates for History and Social Studies in 2008

  34. Seven Steps in Using Data in Advocacy and Systems Change 1. Describe the Problem 2. Generate Vision Data 3. Commit To Benchmarks 4. Identify Places to Intervene: First Order Change? Second Order Change? 6. Evaluate Implementation 5. Select Interventions 7. Monitor Problem Data

  35. Step 3: Commit to Benchmarks The term “benchmarking” was originally used by land surveyors to mark reference points (buildings, rocks, landmarks) measuring the distance from a particular spot. Setting a benchmark told you how far away you were from a certain reference point (Vision Data).

  36. Commit to Benchmarks • What are the yearly markers we will use to determine how far away we are from our goals and vision? (Expected Results Over Time) • How will we know that we are getting closer? What are our measurements?

  37. Commit to Benchmarks

  38. Commit to Benchmarks

  39. Seven Steps in Using Data in Advocacy and Systems Change 1. Describe the Problem 2. Generate Vision Data 3. Commit To Benchmarks 4. Identify Places to Intervene: First Order Change? Second Order Change? 6. Evaluate Implementation 5. Select Interventions 7. Monitor Problem Data

  40. Step 4: Identify Places to Intervene • Level • Student • Peer • Teachers • School Climate and Policies • Family • Community

  41. Seven Steps in Using Data in Advocacy and Systems Change 1. Describe the Problem 2. Generate Vision Data 3. Commit To Benchmarks 4. Identify Places to Intervene: First Order Change? Second Order Change? 6. Evaluate Implementation 5. Select Interventions 7. Monitor Problem Data

  42. Step 5: Select Interventions • Identify Possible Interventions • Individual Interventions • Systemic Interventions • Align with Levels (Student thru Community) • Align with Evidence Base • Identify Expertise and Learning Needs • Identify Community Resources • Identify Possible Roadblocks and Resistance • Write Action Plan

  43. Seven Steps in Using Data in Advocacy and Systems Change 1. Describe the Problem 2. Generate Vision Data 3. Commit To Benchmarks 4. Identify Places to Intervene: First Order Change? Second Order Change? 6. Evaluate Implementation 5. Select Interventions 7. Monitor Problem Data

  44. Step 6 Evaluate Intervention • Formative Evaluation—Process • Monitor Implementation • Correct Problems • Summative Evaluation • Perceptual Data • Results Data

  45. Seven Steps in Using Data in Advocacy and Systems Change 1. Describe the Problem 2. Generate Vision Data 3. Commit To Benchmarks 4. Identify Places to Intervene: First Order Change? Second Order Change? 6. Evaluate Implementation 5. Select Interventions 7. Monitor Problem Data

  46. Step 7: Monitor Problem Data • Evaluate Results Over Time • Alter Implementation Strategy • Stay the Course • Celebrate • Disseminate • Publicize

  47. Discussion

  48. National Center for School Counseling Outcome Research http://www.umass.edu/schoolcounseling/ Thank You

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