The development of syntactic complexity of Chinese JFL learners based on Mean Dependency Distance and Mean Hierarchical Distance.

  • Published In: IRAL: International Review of Applied Linguistics in Language Teaching, 2024, v. 62, n. 1. P. 79 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Yang, Xiaomin; Li, Wenping 3 of 3

Abstract

Mean dependency distance (MDD) and mean hierarchical distance (MHD) are two linguistic measures used in dependency syntax studies to investigate the syntactic features of compositions written by English as a foreign language (EFL) learners. However, their applicability and validity in differentiating proficiency levels and genre effects among Japanese as a foreign language (JFL) learners remain unknown. This study uses a longitudinal dataset that tracks 110 Chinese JFL learners over 12 months and examines their syntactic development as well as the effects of genres. The results indicate that both MDD and MHD effectively capture developmental and genre effects; moreover, both measures show significantly higher values in argumentative writing than narrative writing. However, the extent of genre effects over time is not the same in MDD and MHD. The findings provide new insights into the developmental characteristics of JFL learners' interlanguage and may contribute to evaluating syntactic complexity and developing automatic evaluation systems. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:IRAL: International Review of Applied Linguistics in Language Teaching. 2024/03, Vol. 62, Issue 1, p79
  • Document Type:Article
  • Subject Area:Language and Linguistics
  • Publication Date:2024
  • ISSN:0019-042X
  • DOI:10.1515/iral-2023-0010
  • Accession Number:175872618
  • Copyright Statement:Copyright of IRAL: International Review of Applied Linguistics in Language Teaching is the property of De Gruyter and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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