چکیده

This study outlines the use of learner corpora in conducting a corpus-driven error analysis to explore authentic learner errors with respect to their frequencies, typology of errors, and contexts in which the errors are regularly seen in learners’ writings. It has been argued that learner-generated texts provide second language researchers and teachers with an invaluable context to investigate and better understand learner interlanguage errors through conducting an error analysis. The necessity of such corpus-driven studies on authentic learner errors in developing teaching materials and designing engaging tasks has also been emphasized. We derived the sentences containing errors from a learner corpus with 5,000 words in total. The corpus is based on the writings that Health Policy (HP) students provided for us in an ESP context at Iran University of Medical Sciences. All participants are intermediate level students whose first language is Persian. In the analysis section, we used AntConc software for measuring error frequencies, text types, and tokens. We also used UAM CorpusTool to produce automatic syntactic analysis of the sentences in our learner corpus. Results indicate that discourse markers (85%), dangling sentences (70%), and the use of punctuation (70%) are three main error categories. We will conclude our presentation with a discussion of how to draw learners’ attention to these errors in writing. In addition, we will suggest techniques that can be incorporated in a number of classroom activities focusing on teaching writing to ESP students alongside some other implications particularly helpful for HP students.

کليدواژه ها

learner corpora , corpus-driven error analysis , error categories

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Afsaneh Dehnad , 1398 , A Computer-aided Error Analysis of Iranian ESP Students’ Writing with a Learner Corpus Perspective , اولين کنفرانس بين المللي مطالعات زبان انگليسي