Hawkins, Jacqueline2018-12-032018-12-03May 20182018-05May 2018http://hdl.handle.net/10657/3604All English Language Learners (ELLs) who strive to access higher education opportunities in the United States need to be English proficient. In addition to a lack of English language proficiency, many ELLs face other challenges such as the time limits due to their visa status and the high costs of living and education in the US. Thus, they would benefit from a curriculum that not only facilitates their language proficiency but also accelerates it. Digitization and the globalization of English have made it possible to incorporate different forms of digital technology into the infrastructure of English language programs. Additionally, since most ELLs in the 21st century are familiar with new technology (and many are tech savvy), applying a digital language learning tool which supports their English language proficiency can facilitate their college readiness. However, in the existing literature, there are no clear criteria for English language programs or individuals to identify and evaluate appropriate language learning technology tools. To fill the gap, this research study proposed empirically-supported guidelines in a rubric called the ULTIA Rubric to help English language programs or individuals identify and evaluate appropriate technology-supported language-learning tools. The ULTIA Rubric has its basis in the major components of the five concepts of Universal Design for Learning, Learning Science, Technology Acceptance Model, Intelligent Tutoring System, and Automatic Speech Recognition and has been used to identify and incorporate aspects of each of these five concepts. It also illustrates how effectively the technology tool under evaluation supports the four major language skills: listening, speaking, reading, and writing. Once the rubric was developed by the researcher and validated by a second researcher, it was used to assess the extent to which the 43 components of the five concepts were present in a language training software called NativeAccent. The results showed that the language training technology tool (a) is systematically structured based on the components of Universal Design for Learning and Learning Science; (b) can be personalized based on the needs of its users and their native language speech patterns; (c) identifies and detects errors through its Automatic Speech Recognition feature; (d) provides immediate feedback through its Intelligent Tutoring System; and (e) supports pronunciation, fluency, and grammar skills directly and listening skill indirectly. Therefore, while in most cases NativeAccent complied with the research-based components of the ULTIA Rubric, future research is needed to determine whether the discrepancies are due to defects in the rubric or in the technology tool.application/pdfengThe author of this work is the copyright owner. UH Libraries and the Texas Digital Library have their permission to store and provide access to this work. Further transmission, reproduction, or presentation of this work is prohibited except with permission of the author(s).English Language Learners (ELLs)Universal Design for Learning (UDL)Learning science (LS)LearningTechnology Acceptance Model (TAM)Intelligent Tutoring System (ITS)Automatic Speech Recognition (ASR)The ULTIA Rubric2018-12-03Thesisborn digital