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

Tyler H. Matta and James Soland
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Tyler H. Matta: Pearson
James Soland: NWEA

Journal of Educational and Behavioral Statistics, 2019, vol. 44, issue 1, 78-102

Abstract: 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.

Keywords: joint modeling; longitudinal data; classification accuracy (search for similar items in EconPapers)
Date: 2019
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Persistent link: https://EconPapers.repec.org/RePEc:sae:jedbes:v:44:y:2019:i:1:p:78-102

DOI: 10.3102/1076998618791259

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