Publication Date
2024
Document Type
Dissertation
Committee Members
Mindy McNutt, Ph.D. (Committee Chair); Grant Hambright, Ed.D. (Committee Co-Chair); Ramzi Nahhas, Ph.D. (Committee Member); Josephine Wilson, D.D.S., Ph.D. (Committee Member)
Degree Name
Doctor of Education (EdD)
Abstract
This dissertation focused on a novel improvement to the current method for adapting assessments into American Sign Language (ASL). Bilingual Deaf adults participated in back translations across the United States, and those back-translation decisions were assessed by human experts to judge similarity in meaning. Translations were compared to original text samples using two types of Latent Semantic Analysis (LSA) models to compute semantic textual similarity (STS) scores, and to calculate weighted Youden Index (WYI) Scores. These scores were used to determine the ideal cutoff to be used when making judgments and compared to human expert decisions. The results revealed that WYI scores calculated using the Bidirectional Encoder Representations from Transformers (BERT) model performed best and effectively predicted expert decisions for 25% of items, thus substantially reducing the need for human review for many items. These results suggest that while there is great promise for using these methods to reduce cognitive load for back-translation tasks, there is still a crucial need for human attention in such tasks. This research points to the potential of machine learning for streamlining the creation of ASL assessments and increasing accessibility for the Deaf community. However, it also underscores the essential role of human experts in ensuring accuracy and cultural sensitivity. While future advancements in machine learning may one day replicate similar human capabilities, a combination of technology and skilled professionals remains crucial for bridging these communication gaps and providing equitable access to services for Deaf individuals.
Page Count
77
Department or Program
Department of Leadership Studies in Education and Organizations
Year Degree Awarded
2024
Copyright
Copyright 2024, some rights reserved. My ETD may be copied and distributed only for non-commercial purposes and may not be modified. All use must give me credit as the original author.
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
