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Using Data from Schools and Child Welfare Agencies to Predict Near-Term Academic Risks

Author

Listed:
  • Julie Bruch
  • Jonathan Gellar
  • Lindsay Cattell
  • John Hotchkiss
  • Phil Killewald

Abstract

This report provides information for administrators, researchers, and student support staff in local education agencies who are interested in identifying students who are likely to have near-term academic problems such as absenteeism, suspensions, poor grades, and low performance on state tests.

Suggested Citation

  • Julie Bruch & Jonathan Gellar & Lindsay Cattell & John Hotchkiss & Phil Killewald, "undated". "Using Data from Schools and Child Welfare Agencies to Predict Near-Term Academic Risks," Mathematica Policy Research Reports 2c2de769f8e44b728e9be5a90, Mathematica Policy Research.
    • Handle: RePEc:mpr:mprres:2c2de769f8e44b728e9be5a90a66267c
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    File URL: https://www.mathematica.org/-/media/publications/pdfs/education/2020/rel_using-data-from-schools-and-child-welfare-agencies.pdf
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    References listed on IDEAS

    as
    1. Matthew T. Johnson & Stephen Lipscomb & Brian Gill, 2015. "Sensitivity of Teacher Value-Added Estimates to Student and Peer Control Variables (Journal Article)," Mathematica Policy Research Reports 4a9776a57ae9477e80df47e7d, Mathematica Policy Research.
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    Cited by:

    1. Lindsay Cattell & Julie Bruch, "undated". "Identifying Students At Risk Using Prior Performance Versus a Machine Learning Algorithm," Mathematica Policy Research Reports f9af4ce29a0946779776a9891, Mathematica Policy Research.

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