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date: 22 November 2017

Abstract and Keywords

The primary goal of this chapter is to contribute to the growing literature on the role that language may (or may not) play in the academic performance of African American students who are speakers of African American English (AAE) by advancing understanding of the mechanisms by which language affects academic achievement. We identify specific structural features of AAE, whose divergences from Standard Classroom English (SCE), we contend, pose problems by creating a significant additional cognitive load for young AAE speakers who are taught and tested in SCE. Specifically, we use a Bayesian Markov Chain Monte Carlo model to explore the hypothesis that the morphosyntactic organization of AAE has significant, variable effects on second grade African American students’ performance on the Woodcock-Johnson-R Test of Applied Problems. Our model predicts that the average student in this group would answer 9 percent more questions correctly if the issue of linguistic interference were removed.

Keywords: African American English (AAE), Standard Classroom English (SCE), academic achievement, academic performance, Woodcock-Johnson-R Test of Applied Problems, Bayesian Markov Chain Monte Carlo model

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