Joint modeling of English proficiency growth and reclassification timing (SREM)¶
Research Method · All research methods
Evidence · 2 claims (2 for) · 1 study (1 associational),q2· 0 of 1 report an effect size · 2 claims rest on one study
Description¶
Because the joint model outperformed the conventional approach, the article supports designing EL policy analytics that estimate proficiency growth and reclassification timing together rather than separately. The SREM treats "student-specific random effects as latent covariates in the time to reclassification model," yielding more accurate predictions of the grade of reclassification.
Accounts¶
- Model English proficiency development jointly with reclassification timing when predicting EL outcomes: Because the joint model outperformed the conventional approach, the article supports designing EL policy analytics that estimate proficiency growth and reclassification timing together rather than separately. The SREM treats "student-specific random effects as latent covariates in the time to reclassification model," yielding more accurate predictions of the grade of reclassification. (Tyler Matta (2018))
Claims¶
- The SREM predicts time to reclassification more accurately than a conventional discrete-time hazard model [+M]
- Information about English-language development is critical for accurately predicting the grade an EL will reclassify [+M]
Related Research Methods¶
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Key Sources¶
- Tyler Matta, James Soland. (2018). Predicting time to reclassification for English learners: A joint modeling approach. Journal of Educational and Behavioral Statistics. https://doi.org/10.3102/1076998618791259
Page metadata
| Field | Value |
|---|---|
type |
research-method |
id |
joint-modeling-of-english-proficiency-growth-and-reclassification-timing-srem |
title |
Joint modeling of English proficiency growth and reclassification timing (SREM) |
description |
Because the joint model outperformed the conventional approach, the article supports designing EL policy analytics that estimate proficiency growth and reclassification timing together rather than separately. |
status |
review |
generated |
by process:settle-candidates on 2026-10-09 |
trust |
unverified — no one has confirmed this page's content |
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