Near-term academic problems targeted include absenteeism, suspensions, poor grades, and low state-test performance¶

Claim · All claims
Evidence · 1 study · associational r? · q2
Currency · newest study 2020 · checked against its sources 2026-10-20 (automated, 1 of 1 entry) · not reviewed by a person

Subclaims¶

q2 i? The report defines the near-term academic problems of interest as absenteeism, suspensions, poor grades, and low performance on state tests. → 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?

Definitional statement from the report's introduction listing the four near-term academic problem types the predictive approach targets. This is a descriptive scope statement, not a tested result; no outcome statistics appear in the provided text.

"identifying students who are likely to have near-term academic problems such as absenteeism, suspensions, poor grades, and low performance on state tests"

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
• A predictive model built on school and child welfare data was assessed for identifying at-risk students in two Allegheny County education agencies — a broader claim this one bears on
• 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 — a broader claim this one bears on
• 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
• The effect of social skills on academic performance is unclear; poor social skills are clearly associated with negative outcomes — related