Reports

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https://hdl.handle.net/10125/49392

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Now showing 1 - 8 of 8
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    Improving Speech-to-Text Transcription of Chinese Podcasts
    (Language Flagship Technology Innovation Center, 2022-08) Schmitt, Elliot
    The internship was spent contributing to the Tech Center’s ongoing podcast project; an ap- plication that will collect language podcasts and extract information from those podcasts that can help language learners and instructors better find relevant language learning materi- als. The podcast audio files are transcribed by software, and most of the work of the intern- ship was creating a markup tool that can im- prove the quality of the podcast transcriptions. The transcriptions were corrected by hand and then a rule-based approach was developed to correct errors the transcription software consis- tently made. This adds a layer of polish to the project, yielding cleaner and more accurate En- glish translations later on. The internship was largely exploratory, and the rest of the time was spent experimenting with other aspects of the project, such as researching lexical sophis- tication and how a metric for the sophistication of a text could be useful information to teach- ers or learners trying to gather useful study ma- terials.
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    VOT-CP: A Python Program for Automatic Data Codification and Calculation of Voice Onset Time
    (Language Flagship Technology Innovation Center, 2021) Gutiérrez Topete, Ernesto
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    The Summarization of Arabic News Texts Using Probabilistic Topic Modeling for L2 Micro Learning Tasks
    ( 2020-01-14) Elsayed, Issa
    The field of Natural Language Processing (NLP) combines computer science, linguistic theory, and mathematics. Natural Language Processing applications aim at equipping computers with human linguistic knowledge. Applications such as Information Retrieval, Machine Translation, spelling checkers, as well as text sum- marization, are intriguing fields that exploit the techniques of NLP. Text summariza- tion represents an important NLP task that simplifies various reading tasks. These NLP-based text summarization tasks can be utilized for the benefits of language acquisition.
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    The Language Flagship Technology Innovation Center Blueprint for Success
    (The Language Flagship Technology Innovation Center, 2019-10-05) The Language Flagship Technology Innovation Center
    The Language Flagship Technology Innovation Center (Tech Center) at the University of Hawai‘i at Mānoa presents this Blueprint for Success in order to help The Language Flagship, as well as other federal initiatives and academic programs interested in enhancing high quality language programs, to improve language learning through the strategic integration of technology. Through multiple symposia and outreach events to promote input and collaboration across the Flagship programs, the Tech Center has worked to make the integration of effective language learning technology central to The Language Flagship mission. The participation and input of Language Flagship directors, instructors and students, along with colleagues from across academia, government and the private sector, has been instrumental in refining our views and practices in the integration of blended learning into high quality instruction. We offer these Goals and Guiding Principles to the Flagship Community to assist our efforts in the integration of best practices in technology-based learning into the overall programmatic approaches throughout academia and the federal and private sectors.
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    Stakeholder Views of the Place of Technology in Flagship Programs.
    (The Language Flagship Technology Innovation Center, 2016) Brown, J. D. ; Trace, J. ; Rodriguez, Julio C.
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    Innovating Language Education: An NMC Horizon Project Strategic Brief
    ( 2016) Adams Becker, Samantha ; Rodriguez, Julio C. ; Estrada, V. ; Davis, A.
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    PERLS Pilot Study Report
    (The Language Flagship Technology Innovation Center, 2017-08-30) Suvorov, Ruslan
    The purpose of this pilot study was to investigate usability of the PERLS (PERvasive Learning System) app and its potential for micro-learning by exploring the nature of Flagship students’ engagement with it, identifying the strengths and weaknesses of its content and technological features, and determining how the app can promote and sustain language learning for both pre- and post-Capstone Flagship students. The pilot study took place during a 2-week period (July 17-August 1, 2017). Participants were 11 pre-Capstone and one post-Capstone Chinese Flagship students who participated in two 30-minute semi-structured interviews conducted at the end of the rst week (Round 1) and at the end of the second week of the pilot study (Round 2). Data obtained from 21 interviews (i.e., 12 interviews from Round 1 and nine interviews from Round 2) were summarily transcribed and qualitatively analyzed to identify emergent themes relevant to the purpose of the pilot study. Based on the analysis of the interview data, this report presents an executive summary of the main ndings and o ers recommendations for improving PERLS.