IDEAS home Printed from https://ideas.repec.org/p/mpr/mprres/f9af4ce29a0946779776a9891d14224c.html
   My bibliography  Save this paper

Identifying Students At Risk Using Prior Performance Versus a Machine Learning Algorithm

Author

Listed:
  • Lindsay Cattell
  • Julie Bruch

Abstract

This report provides information for administrators in local education agencies who are considering early warning systems to identify at-risk students.

Suggested Citation

  • 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.
    • Handle: RePEc:mpr:mprres:f9af4ce29a0946779776a9891d14224c
    as

    Download full text from publisher

    File URL: https://www.mathematica.org/-/media/publications/pdfs/education/2021/rel_identifying-students-at-risk_2021126.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.

      More about this item

      Keywords

      Schools; At-risk Students; Machine Learning; Early Warning System;
      All these keywords.

      NEP fields

      This paper has been announced in the following NEP Reports:

      Statistics

      Access and download statistics

      Corrections

      All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:mpr:mprres:f9af4ce29a0946779776a9891d14224c. See general information about how to correct material in RePEc.

      If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

      If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

      If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

      For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Joanne Pfleiderer or Cindy George (email available below). General contact details of provider: https://edirc.repec.org/data/mathius.html .

      Please note that corrections may take a couple of weeks to filter through the various RePEc services.

      IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.