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Improving Performance: The Five Step Process

Improving Performance: The Five Step Process. Mimi Lufkin: Chief Executive Officer National Alliance for Partnerships in Equity California: December 2-3, 2008. The Five Step Process. Perkins Act Accountability . Core Indicators on Nontraditional CTE

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Improving Performance: The Five Step Process

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  1. Improving Performance: The Five Step Process Mimi Lufkin: Chief Executive Officer National Alliance for Partnerships in Equity California: December 2-3, 2008

  2. The Five Step Process

  3. Perkins Act Accountability Core Indicators on Nontraditional CTE • Participation in CTE programs preparing students for nontraditional fields • Completion of CTE programs preparing students for nontraditional fields

  4. Nontraditional Fields Occupations or fields of work, including careers in computer science, technology, and other current and emerging high skill occupations, for which individuals from one gender comprise less than 25 percent of the individuals employed in each such occupation or field of work.

  5. Document Performance Results GOAL: Understand the problem completely before you seek solutions How do you analyze performance data? What questions should be addressed? What tools and methods can be used to present and analyze data? How should data quality problems be considered in analyzing data?

  6. Perkins Accountability Measure Participation Rate = # underrepresented students participating in NT CTE all students participating in NT CTE OR # of females enrolled in pre-engineering All students (males and females) enrolled in pre-engineering

  7. Perkins Accountability Measure Completion Rate = # underrepresented students completing NT CTE all students completing NT CTE OR # of females completing pre-engineering All students (males and females) completing pre-engineering

  8. Data CollectionDisaggregation required in Perkins IV Special Population • Underrepresented gender students in a nontraditional CTE program • Single Parent • Displaced Homemaker • Limited English Proficiency • Individuals with a Disability • Economically Disadvantaged • Gender • Male • Female • Race/Ethnicity • American Indian or Alaskan Native • Asian or Pacific Islander • Black, non-Hispanic • Hispanic • White- non-Hispanic

  9. Recommended Analyses Trends • At least 2 years • Prefer 3-5 years • Site specific • Statewide • District • School/College • Programs

  10. Benchmarking • State performance level • Best performer in state • National program benchmark • Selected peer benchmark • Set your own benchmark

  11. Perkins Accountability Resources • Peer Collaborative Resource Network • www.edcountability.net • National Alliance for Partnerships in Equity • Growing Pains • Nontraditional SOC/CIP Crosswalk for Males and Females • www.napequity.org

  12. National Program Evaluations • Project Lead the Way • Annual Assessment Report for 2006-07 • Report 17% female enrollment nationally • Cisco Networking Academy Gender Initiative • Set a benchmark for programs at 30% female enrollment • National Center for Women in Information Technology • Report on the Status of Women in IT

  13. California Online Data Sources • Postsecondary • CCCCO Perkins III & IV Data • Secondary • CDE Perkins Website • Data Quest • Cal PASS (future)

  14. Other Data Sources • National Center for Educational Achievement – Just 4 the Kids • National Center for Educational Statistics • Southern Regional Education Board Data Library • NCLB State and Local Report Cards • State and Local Participation and Achievement Data

  15. Perkins Consolidated Annual Report Data State District or College Program Level Analysis

  16. State Level Perkins Nontraditional Data Analysis

  17. Statewide Secondary and Postsecondary Participation Data

  18. Statewide Secondary and Postsecondary Participation Data

  19. Statewide Secondary and Postsecondary Participation and Completion Data

  20. Statewide Secondary and Postsecondary Completion Data

  21. Statewide Secondary and Postsecondary Data • What does the data say or suggest? • How has state performed in each measure? • What happened to the results over time? • What questions arise from this data?

  22. District or College Level Perkins III Analysis

  23. College Participation and Completion Data

  24. College Perkins III Nontraditional Participation Data

  25. College Perkins III Nontraditional Completion Data

  26. College Level Data • How has the college performed on each measure? • How has the college performed as compared to the state average on each measure? • What happened to the results over time? • What questions arise from this data?

  27. Program Level Perkins III Analysis

  28. State Program Participation and Completion Data09 TOP Code- Engineering and Industrial Tech

  29. State Perkins III Nontraditional Participation and Completion Data09 TOP Code Engineering and Industrial Tech

  30. Program Level Data • What does the data say or suggest? • How has the state performed in each measure? • What happened to the results over time? • What questions arise from this data?

  31. College Program Participation and Completion 09 TOP Code- Engineering and Industrial Tech

  32. College Perkins III Nontraditional Participation Data09 TOP Code Engineering and Industrial Tech

  33. College Perkins III Nontraditional Completion Data09 TOP Code Engineering and Industrial Tech

  34. Program Level Data • What does the data say or suggest? • How has the college performed in each measure? • What happened to the results over time? • What questions arise from this data?

  35. Other Analyses with Perkins Data • You can do disaggregated analyses by: • Ethnicity • Special population • Suggested comparisons and analyses: • Look statewide (secondary & postsecondary) and at specific sites and in specific programs • Compare % to see if certain groups choose NTO CTE programs at higher/lower rates than their representation in CTE programs in general • Look at total numbers as well as percents. Be aware of the effect of small sample size

  36. Skill Attainment in Engineering and Industrial Technology

  37. Completions in Engineering and Industrial Technology

  38. Completion Caveats • Completions are tracked for “concentrators” - those students who are seriously committed to the program. • There may be many dropouts before students become concentrators.

  39. Placement for Engineering and Industrial Technology

  40. Employment Retention for Engineering and Industrial Tech

  41. Nontraditional Participation: Special Population AnalysisEngineering and Industrial Tech (09) - Region 6 data

  42. Data Worksheet Activity

  43. Case Study

  44. The Five Step Process

  45. STEP TWO Identify Root Causes

  46. Why Search for Root Causes? • Keep from fixating on the “silver bullet” strategy • Identify the conditions or factors that cause or permit a performance gap to occur • Two types of causes: • Direct cause (i.e. instructional practice) • Indirect cause (i.e. teacher training)

  47. Review Research Summary Root Causes Tables • Chapter 6 Tables 16, 17, 19, and 20 from, “Improving Performance on Perkins III Core Indicators: Summary of Research on Causes and Improvement Strategies” • Authors: Robert Sheets, Mimi Lufkin, and David Stevens for the National Centers for Career & Technical Education • Available at www.stemequitypipeline.org

  48. Review Research Summary Bias Literacy: A review of concepts in research on discrimination By Ruta Sevo, Ph.D. and Daryl E. Chubin, Ph.D. Available at www.stemequitypipeline.org

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