Understanding the At-Risk Indicator: Nevada's Pupil-Centered Funding Plan
Saved in:
| Title: | Understanding the At-Risk Indicator: Nevada's Pupil-Centered Funding Plan |
|---|---|
| Language: | English |
| Authors: | Sean Tanner, Kelsey Krausen, Betsy Garcia, Amanda Brown, WestEd |
| Source: | WestEd. 2025. |
| Availability: | WestEd. 730 Harrison Street, San Francisco, CA 94107-1242. Tel: 877-493-7833; Tel: 415-565-3000; Fax: 415-565-3012; Web site: http://www.wested.org |
| Peer Reviewed: | N |
| Page Count: | 46 |
| Publication Date: | 2025 |
| Document Type: | Reports - Research Numerical/Quantitative Data |
| Education Level: | Elementary Secondary Education |
| Descriptors: | Funding Formulas, Educational Finance, Artificial Intelligence, At Risk Students, Resource Allocation, Academic Achievement, Student Characteristics, School Districts, Institutional Characteristics, Public Schools, Elementary Secondary Education |
| Geographic Terms: | Nevada |
| Abstract: | Nevada's Pupil-Centered Funding Plan (PCFP) is the first in the nation to use a machine learning-based at-risk indicator (the GRAD score) to allocate funding based on students' likelihood of not graduating. The indicator closely aligns with traditional measures of need--such as identifying students with individualized education programs (IEPs), English Learners, and foster youth--but provides a more precise, targeted approach. Because Graduation Related Analytic Data (GRAD) scores change yearly, the specific students receiving at-risk funding can shift, complicating school- and district-level budget planning. While interventions in these schools have improved math and English outcomes overall, gains for students identified as at risk have been more modest, highlighting the need for continued evaluation of how targeted funding translates into improved student achievement. |
| Abstractor: | ERIC |
| Entry Date: | 2026 |
| Accession Number: | ED677573 |
| Database: | ERIC |
| Abstract: | Nevada's Pupil-Centered Funding Plan (PCFP) is the first in the nation to use a machine learning-based at-risk indicator (the GRAD score) to allocate funding based on students' likelihood of not graduating. The indicator closely aligns with traditional measures of need--such as identifying students with individualized education programs (IEPs), English Learners, and foster youth--but provides a more precise, targeted approach. Because Graduation Related Analytic Data (GRAD) scores change yearly, the specific students receiving at-risk funding can shift, complicating school- and district-level budget planning. While interventions in these schools have improved math and English outcomes overall, gains for students identified as at risk have been more modest, highlighting the need for continued evaluation of how targeted funding translates into improved student achievement. |
|---|
