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Facilitating American Sign Language learning for hearing parents of deaf children via mobile devices

In the United States, between 90 and 95% of deaf children are born to hearing parents. In most circumstances, the birth of a deaf child is the first experience these parents have with American Sign Language (ASL) and the Deaf community. Parents learn ASL as a second language to provide their children with language models and to be able to communicate with their children more effectively, but they face significant challenges.

To address these challenges, I have developed a mobile learning application, SMARTSign, to help parents of deaf children learn ASL vocabulary. I hypothesize that providing a method for parents to learn and practice ASL words associated with popular children's stories on their mobile phones would help improve their ASL vocabulary and abilities more than if words were grouped by theme. I posit that parents who learn vocabulary associated with children's stories will use the application more, which will lead to more exposure to ASL and more learned vocabulary.

My dissertation consists of three studies. First I show that novices are able to reproduce signs presented on mobile devices with high accuracy regardless of source video resolution. Next, I interview hearing parents with deaf children to discover the difficulties they have with current methods for learning ASL. When asked which methods of presenting signs they preferred, participants were most interested in learning vocabulary associated with children's stories. Finally, I deploy SMARTSign to parents for four weeks. Participants learning story vocabulary used the application more often and had higher sign recognition scores than participants who learned vocabulary based on word types. The condition did not affect participants' ability to produce the signed vocabulary.

Identiferoai:union.ndltd.org:GATECH/oai:smartech.gatech.edu:1853/47629
Date02 April 2013
CreatorsXu, Kimberly A.
PublisherGeorgia Institute of Technology
Source SetsGeorgia Tech Electronic Thesis and Dissertation Archive
Detected LanguageEnglish
TypeDissertation

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