Phrasal Complexity Measures as Predictors of EFL University Students’ English Academic Writing Proficiency

Q2 Arts and Humanities rEFLections Pub Date : 2020-04-24 DOI:10.61508/refl.v27i1.241750

Krittaya Thongyoi, Kornwipa Poonpon

{"title":"Phrasal Complexity Measures as Predictors of EFL University Students’ English Academic Writing Proficiency","authors":"Krittaya Thongyoi, Kornwipa Poonpon","doi":"10.61508/refl.v27i1.241750","DOIUrl":null,"url":null,"abstract":" The study aims to investigate phrasal complexity measures that can predict EFL students’ academic writing proficiency. Academic English written test responses were derived from written responses from the Khon Kaen University Academic English Language Test (KKU-‐AELT). Five hundred and thirty written responses were separated into groups based on their writing scores. Sixty-‐six phrasal complexity measures (Kyle, 2016) were analyzed for this study. The Tool for the Automatic Analysis of Syntactic Sophistication and Complexity (TAASSC), a computational tool for phrasal complexity analysis, was used to calculate the average numbers of occurring measures in written responses. Phrasal complexity measures occurring in written responses were analyzed with the independent t-‐test. Then, 11 significant phrasal complexity measures, derived from the independent t-‐test, were entered into Binary logistic regression in order to examine potential phrasal complexity measures that can predict proficiency levels. The results revealed three phrasal complexity measures that can predict academic writing for higher proficiency level students. ","PeriodicalId":36332,"journal":{"name":"rEFLections","volume":"125 39","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2020-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"rEFLections","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.61508/refl.v27i1.241750","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"Arts and Humanities","Score":null,"Total":0}

引用次数: 3

Abstract

The study aims to investigate phrasal complexity measures that can predict EFL students’ academic writing proficiency. Academic English written test responses were derived from written responses from the Khon Kaen University Academic English Language Test (KKU-‐AELT). Five hundred and thirty written responses were separated into groups based on their writing scores. Sixty-‐six phrasal complexity measures (Kyle, 2016) were analyzed for this study. The Tool for the Automatic Analysis of Syntactic Sophistication and Complexity (TAASSC), a computational tool for phrasal complexity analysis, was used to calculate the average numbers of occurring measures in written responses. Phrasal complexity measures occurring in written responses were analyzed with the independent t-‐test. Then, 11 significant phrasal complexity measures, derived from the independent t-‐test, were entered into Binary logistic regression in order to examine potential phrasal complexity measures that can predict proficiency levels. The results revealed three phrasal complexity measures that can predict academic writing for higher proficiency level students.

查看原文

微信好友朋友圈 QQ好友复制链接

本刊更多论文

作为 EFL 大学生英语学术写作水平预测指标的短语复杂性测量方法

本研究旨在探讨能够预测 EFL 学生学术写作水平的短语复杂性测量方法。学术英语书面测试的答卷来自孔敬大学学术英语语言测试（KKU--AELT）的书面答卷。五百三十份书面答卷根据其写作分数被分为不同的组别。本研究分析了 66 个短语复杂性测量指标（Kyle，2016 年）。句法复杂性和复杂性自动分析工具（TAASSC）是一种用于短语复杂性分析的计算工具，用于计算书面回答中出现的短语复杂性测量的平均数量。对书面回答中出现的句法复杂性量进行了独立的 t 检验分析。然后，将独立 t 检验得出的 11 个重要的短语复杂度测量值输入二元逻辑回归，以研究能够预测熟练水平的潜在短语复杂度测量值。结果显示，有三种短语复杂性测量方法可以预测高水平学生的学术写作。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文去求助

来源期刊