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The Challenges of Linking K-12 and Post-Secondary Data to Improve Education Outcomes

Education leaders across the country are confronting a growing challenge: too many students are not college ready when they leave high school.

New York City is combining secondary and post-secondary data to trace student outcomes through high school to college.

Although indicators exist to identify students at risk of dropping out of high school, few indicators of students’ college readiness are currently in place, and few districts have linked indicators to practices and policies in ways that would enable action to create meaningful, lasting change.

Leaders in New York are creating a datasharing collaboration between the New York City Department of Education (NYCDOE) and the City University of New York (CUNY) to evaluate the college preparedness of their shared students.

Combining data from both sources aims to answer questions such as:

* What are the outcomes for NYCDOE students after they enroll at CUNY?

* What is the variation in college outcomes and trajectories of students among NYCDOE high schools?

* Which schools have the greatest success in preparing students for college?

* What are the college outcomes and trajectories of students with particular characteristics and achievement histories, such as students who have received a certain type of diploma, participated in Advanced Placement courses, and achieved different scores on standardized tests?

To answer these questions, among others, NYCDOE and CUNY started to share their data in 2008, and with the Leaky Pipeline grant from the Bill & Melinda Gates Foundation in 2010, NYCDOE began to further analyze the college outcomes of its students and the factors that lead to college readiness.

They developed new accountability metrics to identify and refine the kinds of support schools need to provide to prepare their students for college.

Lessons Learned: What to Consider When Institutions Collaborate

1 A core set of researchers within and across institutions saves time and avoids duplication.

2 Fostering collaboration requires good communication between institutions.

3 Institutions must communicate about data exchange and hold one another accountable for timelines.

4 Differences in definitions of populations of interest and cohorts should be clarified and accounted for in findings.

5 Creating common identifiers and shared data warehouses increases the accuracy of data matching.

6 Detailed data documentation avoids duplication and saves time and resources.

7 Careful reconciliation of discrepancies allows collaborations with other agencies/sources of post-secondary data.

8 Budgeting for the cost of collaboration is critical.

Recommendations for College Readiness Data Sharing across Institutions

1 Create a place to house all data for both internal and external audiences such as principals, teachers, school staff, and parents.

2 Conduct trainings for school staff.

3 Establish additional data-exchange relationships or obtain other post-secondary-related data.

http://annenberginstitute.org/sites/default/files/CRIS_Brief2_0.pdf

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