Skip to main content

ASU Electronic Theses and Dissertations


This collection includes most of the ASU Theses and Dissertations from 2011 to present. ASU Theses and Dissertations are available in downloadable PDF format; however, a small percentage of items are under embargo. Information about the dissertations/theses includes degree information, committee members, an abstract, supporting data or media.

In addition to the electronic theses found in the ASU Digital Repository, ASU Theses and Dissertations can be found in the ASU Library Catalog.

Dissertations and Theses granted by Arizona State University are archived and made available through a joint effort of the ASU Graduate College and the ASU Libraries. For more information or questions about this collection contact or visit the Digital Repository ETD Library Guide or contact the ASU Graduate College at gradformat@asu.edu.


Due to the limits of Arizona's secondary education system, English teachers often have to teach Standard English without regard for students' dialects and home languages. This can contribute to a lack of academic success for students who speak nonstandard and stigmatized language varieties. During the discussions that appear in this thesis, I examine pedagogical practices, particularly bidialectalism, that can be used to better teach these students. While these practices can apply to students of all languages and dialects, I focus on their effects on speakers of African American Vernacular English (AAVE). I also present some ways that educators can be …

Contributors
Gersten, Olivia, Adams, Karen, Prior, Matthew, et al.
Created Date
2014

The speech of non-native (L2) speakers of a language contains phonological rules that differentiate them from native speakers. These phonological rules characterize or distinguish accents in an L2. The Shibboleth program creates combinatorial rule-sets to describe the phonological pattern of these accents and classifies L2 speakers into their native language. The training and classification is done in Shibboleth by support vector machines using a Gaussian radial basis kernel. In one experiment run using Shibboleth, the program correctly identified the native language (L1) of a speaker of unknown origin 42% of the time when there were six possible L1s in which …

Contributors
Frost, Wende Kara, Van Gelderen, Elly, Perzanowski, Dennis, et al.
Created Date
2013