Combine in-school and out-of-school data sources to understand and support students flagged as at risk¶

Strategy · All strategies
Evidence · no claims cited

Description¶

The report recommends an approach in which local education agencies use both in-school data and out-of-school data—human services involvement and public benefit receipt—to understand why students are flagged as at risk and to decide how best to support them. It frames predictor analysis as a way to interpret model flags, not just produce them.

Design Implications¶

Context¶

Requirements¶

• Access to both school records and child welfare or human services administrative data

Constraints¶

• Demonstrated in two Allegheny County, Pennsylvania education agencies; generalization beyond that setting is not established in the provided text

Target Learners¶

• K-12 students identified as at risk of near-term academic problems

Target Learning Goals¶

• Timely identification and support of students at risk of absenteeism, suspension, poor grades, or low test performance

Related Strategies¶

• School Child Welfare Data Predictive Risk Model
• Use an early warning system based on linked school and human services data to target resources and lessen risks before they become more serious
• Use linked school and child welfare data to flag students at academic risk in the coming quarter or semester
• Build early-warning indicators and leverage online-learning-system process data to support students before gaps widen in Grade 3
• Use early cheating-risk predictions only for low-stakes support with human review, never for disciplinary decisions

Examples¶

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Key Sources¶

• 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