A partnership developed an approach for identifying at-risk students using school data linked with child welfare, justice, human services, and public benefits data.¶

Claim · All claims
Evidence · 1 study · design r1 · q2
Currency · newest study 2020 · checked against its sources 2026-10-20 (automated, 1 of 1 entry) · not reviewed by a person · overdue for a recheck since 2023: tool and model accuracy changes quickly

Subclaims¶

q2 i? School districts and a county human services agency partnered with REL Mid-Atlantic to build an approach identifying students at risk of near-term academic problems using linked administrative data. → Julie Bruch 2020

Evidence¶

Julie Bruch 2020¶

Julie Bruch, Jonathan Gellar, Lindsay Cattell, John Hotchkiss, and Phil Killewald. (2020). Using Data from Schools and Child Welfare Agencies to Predict Near-Term Academic Risks, Snapshot. Regional Educational Laboratory Mid-Atlantic. https://ies.ed.gov/ncee/edlabs

q2 · i? · design · r1

Snapshot describing the partnership's purpose: Pittsburgh Public Schools, the Propel Schools charter network, and the Allegheny County Department of Human Services worked with REL Mid-Atlantic on "an approach for identifying at-risk students using school data linked with" human services data. No performance results are reported in the snapshot.

"The stakeholders partnered with the Regional Education Laboratory Mid-Atlantic to develop an approach for identifying at-risk students using school data linked with data on child welfare events, justice system involvement, and other human services involvement and public benefits receipt."

Discussion¶

Related Claims¶

• The study uses five years of linked school and child welfare administrative data to predict near-term academic risks, with the two most recent years serving as outcome years — related
• A predictive model built on school and child welfare data was assessed for identifying at-risk students in two Allegheny County education agencies — a narrower finding that bears on this claim
• The report examines how in-school and out-of-school predictors relate individually to each type of near-term academic problem — a narrower finding that bears on this claim
• Near-term academic problems targeted include absenteeism, suspensions, poor grades, and low state-test performance — a narrower finding that bears on this claim
• A predictive model built from school and child welfare data identifies students at risk of near-term academic problems — a narrower finding that bears on this claim
• Agencies built multi-source linked data systems and worked to increase analytic capacity through hiring, vendor partnerships, and training — a broader claim this one bears on
• Inconsistent terminology across agencies and restrictive data-sharing rules are central barriers to identifying and serving highly mobile youth in both states — related
• Insufficient data systems, LCFF funding limits, transportation barriers, and child welfare capacity constraints impede coordinated support for students in foster care — related