A Multisemiotic Investigation of Iranian EFL Teachers’ Turn-allocation Strategies in their Classroom Interactions
محل انتشار: دوماهنامه جستارهای زبانی، دوره: 13، شماره: 3
سال انتشار: 1401
نوع سند: مقاله ژورنالی
زبان: انگلیسی
مشاهده: 170
فایل این مقاله در 24 صفحه با فرمت PDF قابل دریافت می باشد
- صدور گواهی نمایه سازی
- من نویسنده این مقاله هستم
استخراج به نرم افزارهای پژوهشی:
شناسه ملی سند علمی:
JR_LRR-13-3_017
تاریخ نمایه سازی: 9 مهر 1401
چکیده مقاله:
Second/foreign language classroom interaction is believed to have its own idiosyncrasies and peculiarities. Many studies have focused on the importance of turn-taking systems for students to gain and hold the floor. Nevertheless, a limited number of studies has explored teachers’ turn-allocation strategies in their instructional interactions. Motivated by this gap, through the methodological framework of Conversation Analysis (CA), the present study attempted to investigate the frequently employed turn-allocation strategies that Iranian EFL teachers use in their classroom interactions with their students. To this end, a corpus of nine hours of English instruction was video-recorded and analyzed through Sacks et al.’s (۱۹۷۴) model of turn-allocation. The results of in-depth qualitative analysis indicated that Iranian EFL teachers used multiple resources to allocate the turn to their students. More specifically, it was found that Iranian teachers generally allocate turns to their students through directing their gaze towards them as well as nominating them by their names. Moreover, the teachers, in this study, used non-verbal strategies of head nods and pointing gestures to nominate the next speaker to take the turn. The study ends with some implications for the EFL teachers in that they can manage their turn-allocation techniques more efficiently in their instructional interactions.
کلیدواژه ها:
نویسندگان
Farhad Ghiasvand
PhD Candidate of Applied Linguistics, Allameh Tabataba’i University, Tehran, Iran
مراجع و منابع این مقاله:
لیست زیر مراجع و منابع استفاده شده در این مقاله 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 مقاله لینک شده اند :