data disaggregation: for data driven decision making

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

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Data Disaggregation: For Data Driven Decision Making. By Ron Grimes: Special Assistant to the Assistant Superintendent Office of Career and Technical Accountability & Support. What is “data disaggregation” and why should we use it?. Simple definition: - PowerPoint PPT Presentation

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Page 1: Data Disaggregation:  For Data Driven Decision Making

Data Disaggregation: For Data Driven Decision Making

By Ron Grimes:Special Assistant to the Assistant Superintendent

Office of Career and Technical Accountability & Support

Page 2: Data Disaggregation:  For Data Driven Decision Making

What is “data disaggregation” and why

should we use it?

Page 3: Data Disaggregation:  For Data Driven Decision Making

Simple definition:

Looking at data (test scores, etc.) by specific

subgroups.

Page 4: Data Disaggregation:  For Data Driven Decision Making

Data Types

Demographics

Perceptions

Student Learning

School Process

Page 5: Data Disaggregation:  For Data Driven Decision Making

Ways that CTE can Disaggregate

• Gender• Concentration• WorkKeys data• Global 21 Performance Assessment

Data• Placement• Enrollment in concentrations• Stakeholder satisfaction• Return on Investment

Page 6: Data Disaggregation:  For Data Driven Decision Making

CTE Data Concept Map

STUDENTLEARNING

Portfolios

WESTEST

CSO Profiles

Global 21 CTE

Performance

Projects

Work Keys Rubrics

Additional

Brainstorm Examples

Formative Assessments

CTSO State

Performance

Page 7: Data Disaggregation:  For Data Driven Decision Making

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)

Page 8: Data Disaggregation:  For Data Driven Decision Making

Examples of Data Disaggregation

• WorkKeys Assessment DataWhy is there a Zero in math?

Does this score represent

SWD?

Are the LI scores

improving?

Page 9: Data Disaggregation:  For Data Driven Decision Making

Examples of Data Disaggregation

• Global 21 Performance Assessment Data

Which concentrations have the best

results?

Which concentrations did not meet

standard?

Page 10: Data Disaggregation:  For Data Driven Decision Making

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?

Page 11: Data Disaggregation:  For Data Driven Decision Making

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.

Page 12: Data Disaggregation:  For Data Driven Decision Making

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

Page 13: Data Disaggregation:  For Data Driven Decision Making

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

Page 14: Data Disaggregation:  For Data Driven Decision Making

Process DataLEA Plan Analysis

Agency4

3.45%

Business/Industry

4337.07%

Education45

38.79%

Higher Ed.2

1.72%

Parent3

2.59%

Student7

6.03%

*Undetermined12

10.34%

CRAFT Advisory Council Members

LEA Plan Data 2010-2011

116 Advisory Council Members

Agency6

3.43%

Business/Industry

12169.14%

Education30

17.14%

Higher Ed.5

2.86%

Parent2

1.14%Student

52.86%

*Undetermined6

3.42%

CRAFT Advisory Council Members

LEA Plan Data 2010-2011

175 Advisory Council Members

* = Reported but no information provided to determine type

Page 15: Data Disaggregation:  For Data Driven Decision Making

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.

Page 16: Data Disaggregation:  For Data Driven Decision Making

CTE Data Concept Map

PERCEPTIONS

Parent Surveys

Teacher Surveys

Administration

Surveys

Advisory Council

Surveys

Task Forces

Student SurveysInterviews

Additional

Brainstorm Examples

Observations

Page 17: Data Disaggregation:  For Data Driven Decision Making

Ways to Disaggregate

• There a several ways to disaggregate perception data:– For example:

• Student needs• Stakeholder type• Concentration • Teacher• Compare “satisfaction” rating with

performance

Page 18: Data Disaggregation:  For Data Driven Decision Making

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?

Page 19: Data Disaggregation:  For Data Driven Decision Making

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

Page 20: Data Disaggregation:  For Data Driven Decision Making

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

Page 21: Data Disaggregation:  For Data Driven Decision Making

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?

Page 22: Data Disaggregation:  For Data Driven Decision Making

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.

Page 23: Data Disaggregation:  For Data Driven Decision Making

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:

Page 24: Data Disaggregation:  For Data Driven Decision Making

Questions?