{"title":"Accuracy scoring of elicited imitation: A tutorial of automating speech data with commercial NLP support","authors":"Kathy Minhye Kim , Xiaobin Chen , Xiaoyi Liu","doi":"10.1016/j.rmal.2024.100127","DOIUrl":null,"url":null,"abstract":"<div><p>This tutorial demonstrates how to automate the scoring of two oft-used English morphosyntactic forms, <em>be</em>-passive and third person singular -<em>s,</em> using commercial Natural Language Processing services. It focuses specifically on the context of elicited imitation (EI) tests drawing on previously web-collected EI data (Kim & Godfroid, 2023; Kim et al., 2024). We provide step-by-step instructions and example codes covering three key stages of data processing: (1) speech-to-text transcription, (2) identification of morphosyntactic structures, and (3) the scoring algorithm. This method can be applied to various form-based EI scoring schemes or other form-based automatic scoring tasks, enhancing the broader adoption and practical application of automated scoring in both research and educational settings.</p></div>","PeriodicalId":101075,"journal":{"name":"Research Methods in Applied Linguistics","volume":"3 3","pages":"Article 100127"},"PeriodicalIF":0.0000,"publicationDate":"2024-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2772766124000338/pdfft?md5=98ebdf4d256d09f01dd26f134acaa893&pid=1-s2.0-S2772766124000338-main.pdf","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Research Methods in Applied Linguistics","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2772766124000338","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
This tutorial demonstrates how to automate the scoring of two oft-used English morphosyntactic forms, be-passive and third person singular -s, using commercial Natural Language Processing services. It focuses specifically on the context of elicited imitation (EI) tests drawing on previously web-collected EI data (Kim & Godfroid, 2023; Kim et al., 2024). We provide step-by-step instructions and example codes covering three key stages of data processing: (1) speech-to-text transcription, (2) identification of morphosyntactic structures, and (3) the scoring algorithm. This method can be applied to various form-based EI scoring schemes or other form-based automatic scoring tasks, enhancing the broader adoption and practical application of automated scoring in both research and educational settings.
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