Data Disaggregation: For Data Driven Decision Making

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 School Process Student Learning

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

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 Why is there a Zero in math? • WorkKeys Assessment Data Are the LI scores improving? Does this score represent SWD?

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

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

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 DataLEA 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 Interviews Student Surveys Observations Parent Surveys PERCEPTIONS Advisory Council Surveys Teacher Surveys Task Forces Administration Surveys Additional Brainstorm Examples

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 Concentrations Ethnicity Discipline Incidences Drop out rates Gender College going rate Free & reduced lunch status Enrollment Additional Brainstorm Examples 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 We analyzed the May 2011 Administrative Conference Surveys and listened to your needs: • 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

24. Questions?