The Effect of Task Type on Accuracy and Complexity in IELTS Academic Writing

Nguyễn Thúy Lan1,
1 Faculty of English Teacher Education, VNU University of Languages and International Studies, Phạm Văn Đồng, Cầu Giấy, Hanoi, Vietnam

Main Article Content

Abstract

IELTS is one of the most popular international standardized tests of English language proficiency.  Its two academic writing tasks are crucially different in cognitive and linguistic demands, but to date, few studies have compared the influence of their different task demands on test-takers’ performance. In second language research (L2) area, two contrasting theories on task demands are the Limited Attentional Capacity Model which predicts a worse linguistic performance on a more complex task and the Cognition Hypothesis which expects a better performance on a more demanding task. My study examines the effect of task type as an important factor of task complexity on L2 writing in a testing condition. The study was a single-factor, repeated-measures design which compares the performance of 30 L2 writers on task 1 and task 2 of the IELTS Academic writing subtest. The candidates’ writing samples were analyzed using a range of discourse measures focusing on accuracy and complexity. The findings showed that low demanding task (task 1 - graph description) elicited a significantly better performance in terms of accuracy than high demanding task (task 2 - argumentative essay). Meanwhile, the latter was more complex in terms of grammatical subordination and lexical variation.  The current study contributes exploratory findings to the body of knowledge on L2 writing by investigating task complexity embedded in different task types. The use of discourse measurement of accuracy and complexity revealed some IELTS candidates’ language problems related to genre writing. The gained knowledge may help teachers manipulate task features to channel learners’ attention to the area in which they fail.

Keywords: Language testing, writing assessment, IELTS, task type, genre writing, discourse measurement, accuracy, complexity.

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References

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