A predictive model built on school and child welfare data was assessed for identifying at-risk students in two Allegheny County education agencies¶
Claim · All claims
Evidence · 1 study · associationalr?·q2
Currency · newest study 2020 · checked against its sources 2026-10-08 (automated, 1 of 1 entry) · not reviewed by a person · overdue for a recheck since 2023: tool and model accuracy changes quickly
Subclaims¶
Reading the evidence codes
q is the design tier, i the impact, n the sample, and the kind with its r
the rigour judged within that kind: what each code means.
This claim: no standardized effect is recorded in observations/ for it yet, so no benchmark reading is possible.
q2 i? The report assesses how well the predictive model identifies at-risk students using data from a large local education agency and a smaller charter school network. → Julie Bruch 2020
Evidence¶
Julie Bruch 2020¶
Julie Bruch, Jonathan Gellar, Lindsay Cattell, John Hotchkiss, Phil Killewald. (2020). Using Data from Schools and Child Welfare Agencies to Predict Near-Term Academic Risks. Regional Educational Laboratory Mid-Atlantic. https://www.mathematica.org/publications/using-data-from-schools-and-child-welfare-agencies-to-predict-near-term-academic-risks
q2 · i? · associational · r?
Report-level descriptive account of the model assessment, which used data from two local education agencies in Allegheny County, Pennsylvania. The text states the report "assesses how well the model identifies at-risk students" but prints no performance statistics in the provided text.
"The report describes an approach for developing a predictive model and assesses how well the model identifies at-risk students using data from two local education agencies in Allegheny County, Pennsylvania: a large local education agency and a smaller charter school network."
Discussion¶
Related Claims¶
- The report examines how in-school and out-of-school predictors relate individually to each type of near-term academic problem — related
- Near-term academic problems targeted include absenteeism, suspensions, poor grades, and low state-test performance — a narrower finding that bears on this claim
- Using typical measures of school performance may lead state and local decisionmakers astray when identifying schools for support — related
- 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 partnership developed an approach for identifying at-risk students using school data linked with child welfare, justice, human services, and public benefits data. — a broader claim this one bears on
- A predictive model built from school and child welfare data identifies students at risk of near-term academic problems — possibly the same claim (merge candidate)
- Predictive models built in a modelable ecosystem generalize across institutions — related
Page metadata
| Field | Value |
|---|---|
type |
claim |
id |
risk-model-assessed-two-allegheny-agencies |
title |
A predictive model built on school and child welfare data was assessed for identifying at-risk students in two Allegheny County education agencies |
description |
A predictive model built on school and child welfare data was assessed for identifying at-risk students in two Allegheny County education agencies |
status |
review |
evidence_strength |
weak |
newest_evidence |
2020 |
source_checked |
2026-10-08 |
recheck_by |
2023 |
generated |
by process:wiki-ingest on 2026-10-08 |
trust |
unverified — no one has confirmed this page's content |
sources |
1 — julie-bruch-2020 |
Frontmatter is the page's machine-readable record — see the Schema & Guide.