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¶
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
q is the design tier, i the impact, n the sample, and the kind with its r
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This claim: no standardized effect is recorded in observations/ for it yet, so no benchmark reading is possible.
q2 i? The prior performance early warning system and the algorithm using same-percentage risk score cutoffs are similarly accurate, and both successfully identify most students who are chronically absent, have a low GPA, or fail a course. → 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 summary of its comparison study states that the prior performance early warning system and the algorithm with same-percentage cutoffs are "similarly accurate" and that "Both approaches successfully identify most of the students" with chronic absenteeism, low GPA, or course failure.
"The study finds that the prior performance early warning system and the algorithm using the same-percentage risk score cutoffs are similarly accurate. Both approaches successfully identify most of the students who ultimately are chronically absent, have a low grade point average, or fail a course."
Discussion¶
Related Claims¶
- Attendance, behavior, and course performance (the ABC indicators) reliably predict which secondary students are at risk of dropping out — the general claim this finding bears on, with the studies behind it
- 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 — related
- Both prior performance flags and the machine learning algorithm are less accurate when predicting outcomes for students who are Black — related
- Students who have experienced three or more ACEs are more likely to experience chronic absenteeism, behavioral problems, and academic failure — related
Page metadata
| Field | Value |
|---|---|
type |
claim |
id |
prior-performance-ews-similarly-accurate-same-percentage-cutoffs |
title |
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 |
description |
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 |
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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