1. Data Disaggregation: For Data Driven Decision Making By Ron Grimes: Special Assistant to the Assistant Superintendent Office of Career and Technical Accountability & Support

2. What is “data disaggregation” and why should we use it?

3. Simple definition: Looking at data (test scores, etc.) by specific subgroups.

4. Data Types Demographics Perceptions Student Learning School Process

5. Ways that CTE can Disaggregate Gender Concentration WorkKeys data Global 21 Performance Assessment Data Placement Enrollment in concentrations Stakeholder satisfaction Return on Investment

6. CTE Data Concept Map STUDENT LEARNING Portfolios WESTEST CSO Profiles Global 21 CTE Performance Projects Work Keys Rubrics Additional Brainstorm Examples Formative Assessments CTSO State Performance

7. Ways to Disaggregate There a several ways to disaggregate student learning data: – For example: Gender Socio-economic status Mobility (students moving between schools) Race & ethnicity Students with special needs English as a Second Language (ESL) Successful completion of a course(s)

8. Examples of Data Disaggregation WorkKeys Assessment Data Why is there a Zero in math? Does this score represent SWD? Are the LI scores improving?

9. Examples of Data Disaggregation Global 21 Performance Assessment Data Which concentrations have the best results? Which concentrations did not meet standard?

10. Important Questions There are important questions that student learning data disaggregation can answer. – For example: Is there an achievement gap among our students? Is that gap growing or shrinking? What do enrollment levels in particular concentrations tell us? Are students with special needs adequately represented?

11. Data & Confidentiality Be careful about the data you have access to and its security. FERPA guidelines are very specific regarding specific types student data and its security. Any testing data that includes identifying information or information regarding exceptionality, socio- economic status, etc. cannot be use publicly and limited access can only be granted for professional use only.

12. CTE Data Concept Map SCHOOL PROCESSES CTSO Interventions Counseling Global 21 CTE Performance Process Project-based Learning Work Keys Process Strategic Plan Additional Brainstorm Examples Professional Development Discipline LEA Advisory Council Strategies

13. Ways to Disaggregate Process Data There a several ways to disaggregate process data: – For example – LEA Process – Database for Composite & Individual School/County analysis: Use of Perkins funds Programs of Study Academic and Technical Skill strategies Professional Development Methods of Consultation Program Evaluation methods Access Non-traditional preparation Career Guidance & Academic counseling

14. Process Data LEA Plan Analysis

15. Important Questions There are important questions that process data disaggregation can answer. – For example: Does the use of WIN as an academic technical skill strategy impact Work Keys scores? How many schools are implementing academic integration workshops? Is there an increased placement percentage with schools that offer industry credentials? How many advisory council members represent business/industry in the state.

16. CTE Data Concept Map PERCEPTIONS Parent Surveys Teacher Surveys Administration Surveys Advisory Council Surveys Task Forces Student Surveys Interviews Additional Brainstorm Examples Observations

17. Ways to Disaggregate There a several ways to disaggregate perception data: – For example: Student needs Stakeholder type Concentration Teacher Compare “satisfaction” rating with performance

18. Important Questions There are important questions that perception data disaggregation can answer. – For example: Why are students enrolling in particular concentrations? What trends are identified in the labor market based on advisory council surveys? How satisfied are our stakeholders (measurable for trend analysis)? What strategies for improvement do the stakeholders suggest?

19. CTE Data Concept Map DEMOGRAPHICS Attendance Discipline Incidences Enrollment Gender Ethnicity Free & reduced lunch status Concentrations Additional Brainstorm Examples Drop out rates College going rate Placement

20. Ways to Disaggregate There a several ways to disaggregate demographic data: – For example: Gender Socio-economic status Mobility (students moving between schools) Race & ethnicity Labor market data County educational attainment Postsecondary education completion data

21. Important Questions There are important questions that demographic data disaggregation can answer. – For example: What percentage of students are enrolling in postsecondary education and graduating? Is there a decline in the county population? What adult concentrations would benefit the community based on labor market data?

22. Other Important Questions Disaggregated data can also tell you whether student mobility, professional development of teachers, or parental involvement is affecting student performance. Data can zero-in on information at the school level, the classroom level, the teacher level, the instructional level, etc.

23. EXCITING NEW DATA TOOLS Data Profile – Longitudinal Data Online LEA Plan- user friendly Promising/Best Practices Guide State-wide Perception Surveys and Analysis CTSO Results & Performance Analysis Technology Resources – Usage & Impact on Performance We analyzed the May 2011 Administrative Conference Surveys and listened to your needs:

24. Questions?