Predicting Time to Reclassification for English Learners: A Joint Modeling Approach | Zendy

Tyler Matta | Zendy; James Soland | Zendy

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Predicting Time to Reclassification for English Learners: A Joint Modeling Approach

Author(s) -

Tyler Matta,

James Soland

Publication year - 2018

Publication title -

journal of educational and behavioral statistics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 3.066

H-Index - 59

eISSN - 1935-1054

pISSN - 1076-9986

DOI - 10.3102/1076998618791259

Subject(s) - random effects model , covariate , computer science , mathematics education , statistics , econometrics , psychology , medicine , machine learning , mathematics , meta analysis

The development of academic English proficiency and the time it takes to reclassify to fluent English proficient status are key issues in English learner (EL) policy. This article develops a shared random effects model (SREM) to estimate English proficiency development and time to reclassification simultaneously, treating student-specific random effects as latent covariates in the time to reclassification model. Using data from a large Arizona school district, the SREM resulted in predictions of time to reclassification that were 93% accurate compared to 85% accuracy from a conventional discrete-time hazard model used in prior literature. The findings suggest that information about English-language development is critical for accurately predicting the grade an EL will reclassify.

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