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¶
Claim · All claims
Evidence · 1 study · associationalr?·q2
Currency · newest study 2021 · not yet checked against its sources · not reviewed by a person · overdue for a recheck since 2024: tool and model accuracy changes quickly
Subclaims¶
Reading the evidence codes
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q2 i? The algorithm with 10-percent cutoffs is good at targeting the students most likely to experience an academic problem and has the advantage in predicting suspensions, which are rarer and harder to predict than other outcomes. → Lindsay Cattell 2021
Evidence¶
Lindsay Cattell 2021¶
Lindsay Cattell, Julie Bruch. (2021). Identifying Students At Risk Using Prior Performance Versus a Machine Learning Algorithm. Regional Educational Laboratory Mid-Atlantic, U.S. Department of Education, Institute of Education Sciences. https://ies.ed.gov/ncee/edlabs
q2 · i? · associational · r?
The report's findings summary states the algorithm with 10-percent cutoffs is "good at targeting the students who are most likely to experience an academic problem" and holds the advantage for predicting suspensions, described as rarer and harder to predict.
"In contrast, the algorithm with 10-percent cutoffs is good at targeting the students who are most likely to experience an academic problem; this approach has the advantage in predicting suspensions, which are rarer and harder to predict than the other outcomes."
Discussion¶
Related Claims¶
- A prior performance early warning system and a machine learning algorithm with same-percentage risk-score cutoffs are similarly accurate at identifying students who experience academic problems — related
- Near-term academic problems targeted include absenteeism, suspensions, poor grades, and low state-test performance — related
- The report examines how in-school and out-of-school predictors relate individually to each type of near-term academic problem — related
- Both prior performance flags and the machine learning algorithm are less accurate when predicting outcomes for students who are Black — related
- Students at highest estimated risk of suspension felt safer when suspensions were reduced, while students at lowest risk saw the largest test score gains, with no evidence of overall academic harm — related
Page metadata
| Field | Value |
|---|---|
type |
claim |
id |
algorithm-ten-percent-cutoffs-targets-highest-risk |
title |
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 |
description |
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 |
status |
review |
evidence_strength |
moderate |
newest_evidence |
2021 |
recheck_by |
2024 |
generated |
by process:wiki-ingest on 2026-10-08 |
trust |
unverified — no one has confirmed this page's content |
sources |
1 — lindsay-cattell-2021 |
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