Morphological Complexity Across Descriptive, Expository, and Narrative Text Types in Iranian Lower-Intermediate Language Learners
Publish place: Issues in Language Teaching، Vol: 10، Issue: 1
Publish Year: 1400
نوع سند: مقاله ژورنالی
زبان: English
View: 236
This Paper With 31 Page And PDF Format Ready To Download
- Certificate
- من نویسنده این مقاله هستم
استخراج به نرم افزارهای پژوهشی:
شناسه ملی سند علمی:
JR_ILT-10-1_008
تاریخ نمایه سازی: 2 بهمن 1400
Abstract:
Morphological complexity is one of the dimensions of complexity that has been increasingly analyzed over the last few years. However, results from previous studies drawing on only a single text type are inconclusive. The purpose of this study was to determine the effect of text types (descriptive, narrative, and expository) on the morphological complexity of essays written by Iranian English language learners. The participants included ۸۷ lower-intermediate male and female L۲ learners at six language institutes in Qazvin, Iran, who were selected from ۱۲۷ language learners taking an Oxford Quick Placement test. The participants wrote on each text type in three consecutive weeks as a part of their classroom activity. The morphological complexity of verbs and nouns was separately calculated using the morphological complexity index. The data were analyzed using a series of Friedman and Wilcoxon Signed Rank Tests. The findings did not show any statistically significant differences across text types for nominal inflectional diversity; however, verbal inflectional diversity was statistically significant across text types, with narrative essays morphologically more complex than descriptive and expository essays. The findings may have theoretical and pedagogical implications for researchers and L۲ teachers.
Keywords:
Authors
رجب اسفندیاری
Associate Professor of Applied Linguistics, Imam Khomeini International University, Qazvin, Iran
هاجر جعفری
MA in TEFL, Imam Khomeini International University, Qazvin, Iran
مراجع و منابع این Paper:
لیست زیر مراجع و منابع استفاده شده در این Paper را نمایش می دهد. این مراجع به صورت کاملا ماشینی و بر اساس هوش مصنوعی استخراج شده اند و لذا ممکن است دارای اشکالاتی باشند که به مرور زمان دقت استخراج این محتوا افزایش می یابد. مراجعی که مقالات مربوط به آنها در سیویلیکا نمایه شده و پیدا شده اند، به خود Paper لینک شده اند :