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7/17/2012. 2. Policy Questions on Your Mind?. How many students drop out after 8th grade?Which schools or classrooms produce the greatest academic growth for students?What teacher preparation and training programs have the greatest impact on student achievement?What percentage of each high school
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1. Data Quality Campaign:Improving the Quality, Accessibility and Use of Data in Education Alliance for Excellent Education Data Symposium
March 9, 2006
Jackie Lain, Standard & Poors
Aimee R. Guidera, NCEA & DQC_
2. 7/17/2012 2 Policy Questions on Your Mind… How many students drop out after 8th grade?
Which schools or classrooms produce the greatest academic growth for students?
What teacher preparation and training programs have the greatest impact on student achievement?
What percentage of each high school’s graduates take remedial courses in college?
3. 7/17/2012 3 Introducing The Data Quality Campaign Purpose: Build Support and Political Will Among Policymakers to:
Fully develop high-quality longitudinal data systems in every state by 2009
Increase understanding and promote the valuable uses of longitudinal and financial data to improve student achievement
Promote, develop, and use common data standards and efficient data transfer and exchange
4. 7/17/2012 4 The Power of Longitudinal Data Longitudinal Data — data gathered on the same student from year to year — makes it possible to:
Follow individual student academic growth
Determine the value-added of specific programs
Identify consistently high-performing schools/classrooms/systems worthy of study
5. 7/17/2012 5 10 Essential Elements
A unique statewide student identifier
Student-level enrollment, demographic and program participation information
The ability to match individual students’ test records from year to year to measure academic growth
Information on untested students
A teacher identifier system with the ability to match teachers to students
Student-level transcript information, including information on courses completed and grades earned
Student-level college readiness test scores
Student-level dropout and graduation data
The ability to match student records between the pre-K–12 and higher education systems
A state data audit system assessing data quality, accuracy, and reliability
6. 7/17/2012 6 Fundamental Design & (not so) Future Issues regarding Quality Data Systems Fundamental Design Issues
Privacy Protection
Data Architecture
Data Warehousing
Interoperability
Portability
Professional Development around Data Processes and Use
Researcher Access
Future Issues
Connect school performance with spending
Connect school performance to employment and other systems
Transfer records across systems and states
7. 7/17/2012 7 Sample “Student Mobility and Attrition” Question
What percent of economically disadvantaged students graduated, based on the NGA graduation rate definition?
8. 7/17/2012 8 NGA Graduation Rate Formula
number of students in the cohort graduating in year t________
(first-time ninth-graders in year t – 4*) + (transfers in) – (transfers out)
*Calendar year t - 4 (Fall 2001 for Spring 2005 graduates)
9. 7/17/2012 9 Data Elements Required to Answer the“NGA Graduation Rate” Question A unique statewide student identifier
Student-level enrollment, demographic and program participation information
8. Student-level graduation and dropout data
10. A state data audit system assessing data quality, validity and reliability
According to the 2005 NCEA Survey of State Data Systems, fifteen states have all of these elements
10. 7/17/2012 10 Sample Academic Growth Question
What was the academic growth of economically disadvantaged students taught by recent graduates of your teacher preparation program?
11. 7/17/2012 11 Data Elements Required to Answer Academic Growth Question A unique statewide student identifier
Student-level enrollment, demographic and program participation information
The ability to match individual students’ test records from year to year to measure academic growth
Information on untested students
A teacher identifier system with the ability to match teachers to students
According to the 2005 NCEA Survey of State Data Systems, five states have all of these elements
12. 7/17/2012 12 Future Issues – Connecting Longitudinal and Financial Data Systems Longitudinal Data Systems identify who but not why
First Step: Which schools are successful?
Next Step: What does it take to deliver what works?
Connect processes to results (e.g., curriculum, teacher training)
Connect spending to results (e.g., where/how are successful schools spending their money?)
School level
Program level
13. 7/17/2012 13 Sample Financial Data Questions Do districts with schools that have increased their success with student cohorts over time spend more money on staff development than other districts?
Do patterns of improvement in student outcomes correspond to changes in overall spending levels or specific spending allocations?
14. 7/17/2012 14 DQC Managing Partners
15. 7/17/2012 15 Data Quality Campaign Approach Build Policymaker understanding and will to invest in and use quality data infrastructures
Success Stories
Recognition of leadership
Provide tools, materials and information
Examples of the powerful use of data to inform policy & practice
ToolKits for various audiences on uses of data to improve achievement
Create national forum to ensure collaboration, develop consensus and reduce duplication of effort
Leverage existing efforts to maximize impact
Collaborate/communicate through national partnership whenever possible
One-stop resource center: www.DataQualityCampaign.org
16. 7/17/2012 16 State of the State Data Systems This map shows how many of these essential elements each state currently has based on responses to the 2005 NCEA data system survey.
17. 7/17/2012 17 Survey Results of State Data Systems State of Euphoria - Summary of the Ten Elements
Elements present in sample state in 2005-06?
A unique statewide student identifier YES
Student-level enrollment, demographics and program participation information YES
The ability to match individual students’ test records from year to year to measure academic growth YES
Information on untested students NO
A teacher identifier system with the ability to match teachers to students NO
Student-level transcript information, including information on courses completed and grades earned NO
Student-level college readiness test scores NO
Student-level graduation and dropout rate YES
The ability to match student records between the Pre-K – 12 and higher education systems YES
A state data audit system assessing data quality, validity, and reliability YES
18. 7/17/2012 18 Policy Implications of Survey Results
Does your system have the data in 2005-06 to address these issues using student-level longitudinal data?
Identify which schools produce the strongest academic growth for their students. NO (21 states can)
Know what achievement levels in middle school indicate that a student is on track to succeed in rigorous courses in high school. NO (3 states can)
Calculate each school's graduation rate, according to the 2005 National Governor's Association graduation compact? YES (15 states can)
Determine which high school performance indicators (e.g., enrollment in rigorous courses or performance on state tests) are the best predictors of students' success in college or the workplace. NO (2 states can)
Identify the percentage of high school graduates who go on to college take remedial courses. YES (8 states can)
Identify which teacher preparation programs produce the graduates whose students have the strongest academic growth. NO (5 states can)
19. 7/17/2012 19 Campaign Resources www.DataQualityCampaign.org
White Paper & Brochure on Building State Longitudinal Data Systems
State Data Systems Survey Results
Calendar of Data-Focused Events & Meetings
Resource Center highlighting existing work on data quality and use
Overview of the Campaign & Partners
20. 7/17/2012 20 Contact the DQC: Aimee R. Guidera, National Center for Educational Accountability; DQC Project Director
aguidera@just4kids.org
Jackie Lain, Standard & Poor’s School Evaluation Services
Jackie.Lain@sandp.com