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Using Longitudinal Data to Support State Education Policymaking

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Contacts:

Dr. Allen Ruby
(202) 245-8145
Allen.Ruby@ed.gov

Dr. James Benson
(202) 245-8333
James.Benson@ed.gov

Description:

REQUEST FOR APPLICATIONS: PDF File FY 2021 84.305S (PDF: 244 KB)

Through the Using Longitudinal Data to Support State Policymaking (Using Data) grant program, the Institute of Education Sciences (IES) seeks to expand state education agencies' use of their state longitudinal data systems (SLDS) to provide evidence for use when making policy decisions. State education agencies (SEAs) can apply for these grants, on their own or in collaboration with other organizations, to analyze the data in their own SLDS in order to examine long-term trends in key issues, programs, and policies affecting learner outcomes.

SLDSs are designed to help states, districts, schools, educators, and other stakeholders to make data-informed decisions to improve student learning and outcomes as well as to close achievement gaps. Over the past 15 years and through 6 rounds of SLDS funding, 47 states, the District of Columbia, Puerto Rico, the Virgin Islands, and American Samoa have received at least one SLDS grant. IES has supported and continues to support research use of SLDSs through its research grants programs and its Grants for Statewide Longitudinal Data Systems program.

The Using Data grant program extends IES support for using SLDSs to address research questions that have practical implications for State decision making. The research should be aligned with what the SEA wants to know about a key issue, program, or policy, and should leverage opportunities that emerge as SLDSs add more cohorts and improve their measures of educational programming and student outcomes. The type of research may take different forms. For example, follow up studies may track students in existing studies for longer periods of time or analyze broader sets of outcome measures. Exploratory analyses of the progress of students in previous cohorts may identify factors or pathways that could predict successful student outcomes for future cohorts. Quasi-experimental studies of past or present cohorts may identify promising programs or intervention strategies.

IES intends for this program to improve SEAs' use of evidence to drive decision making and educational programming.