Improving Data Infrastructure to Meet Student and Learner Information Needs
Summary
The Congress should dedicate $1 billion, 1 percent of the proposed workforce funding under the American Jobs Plan, for needed upgrades to Statewide Longitudinal Data Systems (SLDS). Major upgrades are needed to Statewide Longitudinal Data Systems to enable states to effectively monitor and address long-term pandemic learning loss, while ensuring this generation of students stays on track for college and career in the aftermath of the pandemic. With the major influx of planned resources into K12 and postsecondary education from the recent and upcoming relief bills, there is also a critical need to ensure those funds are targeted toward students and workers who are most in need and to measure the impact of those funds on pandemic recovery. Some states, such as Texas and Rhode Island, are already leveraging funds from previous relief bills (e.g., Governor’s Emergency Education Relief Fund from the Coronavirus Aid, Relief, and Economic Security, or CARES Act), to modernize their data systems, offering a model for other states to connect education, workforce, and social services information. This demonstrates an interest and need among states for SLDS upgrades, though additional investment is necessary to address historically underfunded data infrastructure.
We need to overhaul the standardized testing and score reporting system to be more accessible to all of the end users of standardized tests: educators, students, and their families.
Moving postsecondary education data collection to the states is the best way to ensure that the U.S. Department of Education can meet its legislative mandates in an era of constrained federal resources.
Supporting children’s development through health, nutrition, education, and protection programs helps the U.S. achieve its national security and economic interests, including the Administration’s priorities to make America “safer, stronger, and more prosperous.”
To strengthen federal–state alignment, upcoming AI initiatives should include three practical measures: readiness assessments before fund distribution, outcomes-based contracting tied to student progress, and tiered implementation support reflecting district capacity.