A predictive model built from school and child welfare data identifies students at risk of near-term academic problems¶
Claim · All claims
Evidence · 1 study · associationalr?·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¶
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? A predictive model developed from school and child welfare data was assessed for how well it identifies at-risk students facing academic problems in the next quarter or semester. → Regional Educational Laboratory Mid-Atlantic 2020
Evidence¶
Regional Educational Laboratory Mid-Atlantic 2020¶
Regional Educational Laboratory Mid-Atlantic. (2020). Using School and Child Welfare Data to Predict Near-Term Academic Risks: Fact Sheet. Regional Educational Laboratory Mid-Atlantic. https://www.mathematica.org/publications/using-school-and-child-welfare-data-to-predict-near-term-academic-risks-fact-sheet
q2 · i? · associational · r?
This fact sheet, published by the Regional Educational Laboratory Mid-Atlantic, summarizes a companion report: it "details the approach used to develop a predictive model and assesses how well the model identifies at-risk students." No performance statistics are printed in the fact sheet itself.
"It draws on a report that details the approach used to develop a predictive model and assesses how well the model identifies at-risk students."
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 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 on school and child welfare data was assessed for identifying at-risk students in two Allegheny County education agencies — possibly the same claim (merge candidate)
- Near-term academic problems targeted include absenteeism, suspensions, poor grades, and low state-test performance — 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 — related
- Predictive models built in a modelable ecosystem generalize across institutions — related
Page metadata
| Field | Value |
|---|---|
type |
claim |
id |
school-child-welfare-data-predict-near-term-academic-risks |
title |
A predictive model built from school and child welfare data identifies students at risk of near-term academic problems |
description |
A predictive model built from school and child welfare data identifies students at risk of near-term academic problems |
status |
review |
evidence_strength |
weak |
newest_evidence |
2020 |
source_checked |
2026-10-20 |
recheck_by |
2023 |
generated |
by process:wiki-ingest on 2026-10-08 |
trust |
unverified — no one has confirmed this page's content |
sources |
1 — regional-educational-laboratory-mid-atlantic-2020 |
Frontmatter is the page's machine-readable record — see the Schema & Guide.