The report examines how in-school and out-of-school predictors relate individually to each type of near-term academic problem¶
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
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? In-school variables (performance, behavior, and consequences) and out-of-school variables (human services involvement and public benefit receipt) are examined individually in relation to each type of near-term academic problem. → 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?
Descriptive statement of the report's predictor analysis, which separates in-school variables (performance, behavior, consequences) from out-of-school variables (human services involvement, public benefit receipt) and relates each to each academic problem type. No coefficients are printed in the provided text.
"It also examines which types of predictors— in-school variables (performance, behavior, and consequences) and out-of-school variables (human services involvement and public benefit receipt)—are individually related to each type of near-term academic problem"
Discussion¶
Related Claims¶
- Near-term academic problems targeted include absenteeism, suspensions, poor grades, and low state-test performance — related
- A predictive model built on school and child welfare data was assessed for identifying at-risk students in two Allegheny County education agencies — related
- A machine learning algorithm with 10-percent risk-score cutoffs better targets students most likely to experience academic problems and has the advantage in predicting suspensions — 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 — related
- Of 259 predictors analyzed for enrollment prediction, four stand out as most important: prior cohort sizes, in-school suspensions, out-of-school suspensions, and absences — related
Page metadata
| Field | Value |
|---|---|
type |
claim |
id |
in-school-out-of-school-predictors-examined |
title |
The report examines how in-school and out-of-school predictors relate individually to each type of near-term academic problem |
description |
The report examines how in-school and out-of-school predictors relate individually to each type of near-term academic problem |
status |
review |
evidence_strength |
weak |
newest_evidence |
2020 |
source_checked |
2026-10-20 |
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.