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

Lindsay Cattell and Julie Bruch

Mathematica Policy Research Reports from Mathematica Policy Research

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

Keywords: Schools; At-risk Students; Machine Learning; Early Warning System (search for similar items in EconPapers)
Pages: 38
New Economics Papers: this item is included in nep-big and nep-ure
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