Hedges in English for Academic Purposes: A Corpus-based study of Iranian EFL learners

International Journal of Foreign Language Teaching and Research

دوره 8، شماره 33، خرداد 2020، صفحه 109-122   

نوع مقاله: Research Papers

نویسندگان

Hossein khazaee¹؛ Parviz Maftoon^{* 1}؛ Parviz Birjandi¹؛ Ghafour Rezaie Golandoz²

¹Department of English, Science and Research Branch, Islamic Azad University, Tehran, Iran

²Department of English, Garmsar Branch, Islamic Azad University, Garmsar, Iran

چکیده

Hedges, as tools to express tentativeness and doubt, have been studied in plenty of research papers in the Iranian EFL research setting. However, their use in a learner corpus, portraying Iranian learner English, is in need of more research attention. With this end in view, this study aimed at investigating how Iranian EFL learners who have majored in English-related fields in Iran deployed hedges in their academic, expository essays. This study was conducted through running the corpus analysis software MonoConc Pro-Semester version 2.2 on the electronically compiled Iranian Corpus of Learner English, totaling 436,035 words. Automatic and manual analyses suggested that hedges comprised only 7.4% of the total metadiscourse in the Iranian Corpus of Learner English, with 0.68 occurrences per 1,000,000 words. In a comparable native corpus, a sub-corpus of the British Academic Written English, hedges were used with 1.43 occurrences per 1,000,000 words (21% of the total metadiscourse in the corpus). Log-likelihood statistical analysis confirmed statistically significant differences between the two corpora in terms of the use of hedges, with underuse of hedges in the Iranian academic, expository essays relative to the English natives’ essays. Implementations of the results for English academic writing instruction including genre-based, explicit teaching of hedges through data-driven techniques with the aid of tools such as AntConc software and corpora such as the BAWE are considered.

کلیدواژه‌ها

Academic writing؛ Corpus Builder Software؛ hedges؛ Iranian corpus of learner English؛ learner corpora

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