At-risk identification is demographically patterned, with Black, Hispanic, and male students more likely to be identified¶

Claim · All claims
Evidence · 1 study · associational r2 · q2
Currency · newest study 2025 · checked against its sources 2026-10-16 (automated, 1 of 1 entry) · not reviewed by a person

Subclaims¶

q2 i? In 2023/24, 46 percent of Black/African American and 23 percent of Hispanic/Latino students were identified as At-risk, versus 9 percent of White and 4 percent of Asian students; 24 percent of male students versus 17 percent of female students were identified. → Tanner 2025

Evidence¶

Tanner 2025¶

Tanner, S., Krausen, K., Garcia, B., & Brown, A. (2025). Understanding the At-risk indicator: Nevada's Pupil-Centered Funding Plan. WestEd. https://eric.ed.gov/?id=ED677573

q2 · i? · associational · r2

Descriptive analysis of 2023/24 statewide data, reported in text and Figure 1, of At-risk identification rates by race/ethnicity and gender. "Twenty-three percent (close to one in four) of Hispanic/Latino students and 46 percent of Black/African American students across the state were identified as At-risk," compared with 9 percent of White and 4 percent of Asian students; 24 percent of male versus 17 percent of female students. Descriptive rates only, no inferential statistics.

"Twenty-three percent (close to one in four) of Hispanic/Latino students and 46 percent of Black/African American students across the state were identified as At-risk (Figure 1)."

Discussion¶

Learner Variables¶

• Gender — predictor: outcomes differ by group: a gap reflecting circumstances and treatment, not a cause

Related Claims¶

• Both prior performance flags and the machine learning algorithm are less accurate when predicting outcomes for students who are Black — the general claim this finding bears on, with the studies behind it

• Cultural processes and forms of epistemic injustice should be taken into account in understanding how inequalities in education are produced and maintained — the general claim this finding bears on, with the studies behind it

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• GRAD-score-based At-risk identification shows larger proportional year-to-year churn than direct certification would — related
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