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.
Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
