Publication: Phrasal Complexity Measures as Predictors of EFL University Students’ English Academic Writing Proficiency
Submitted Date
Received Date
Accepted Date
Issued Date
2020
Copyright Date
Announcement No.
Application No.
Patent No.
Valid Date
Resource Type
Edition
Resource Version
Language
en
File Type
No. of Pages/File Size
ISBN
ISSN
1513-5934 (Print), 2651-1479 (Online)
eISSN
DOI
Scopus ID
WOS ID
Pubmed ID
arXiv ID
item.page.harrt.identifier.callno
Other identifier(s)
Journal Title
rEFLections Journal
Volume
27
Issue
1
Edition
Start Page
44
End Page
61
Access Rights
Access Status
Rights
Rights Holder(s)
Physical Location
Bibliographic Citation
Research Projects
Organizational Units
Authors
Journal Issue
Title
Phrasal Complexity Measures as Predictors of EFL University Students’ English Academic Writing Proficiency
Alternative Title(s)
Author(s)
Author’s Affiliation
Author's E-mail
Editor(s)
Editor’s Affiliation
Corresponding person(s)
Creator(s)
Compiler
Advisor(s)
Illustrator(s)
Applicant(s)
Inventor(s)
Issuer
Assignee
Other Contributor(s)
Series
Has Part
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.