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AUTOMATED PREDICTION OF THE WRITTEN ERRORS OF TERTIARY LEVEL ESL AND EFL LEARNERS

Abstract

This paper summarizes a two-year project at the Chinese University of Hong Kong (CUHK) from 2006 to 2008. IELTS Online Writing Assistant (IOWA) is a computer-based teaching system designed for students in support of their preparation for the writing module of the International English Language Testing System (IELTS). Situated in Error Analysis (EA) (Corder, 1981; Mitchell, 2004; Lightbown, 1998) and Computer-Assisted Instruction (CAI), the system is designed to address both students' local (i.e., word and sentence) and global (i.e., discourse) errors. It attempts to predict which types of error tertiary level ESL and EFL learners in Hong Kong are most likely to make by assessing their ability to find errors in a prepared script. There are two major research questions: How effective is the system in predicting students' predispositions to committing writing errors and to creating discourse problems? How can teachers better use such a diagnostic tool to complement their teaching practice to address different issues of error categories? The study concludes that: (1) its predictive performance varies greatly depending on the types of error; (2) testing alone—with limited feedback—is effective at reducing the incidence of certain types of error, especially low-frequency and structural errors, in students’ productive writing; (3) certain types of error, which are most difficult for IOWA to predict, could be better instructed by teachers in an ordinary classroom context. Overall, the study has laid important foundations for enhancing our students’ IELTS writing test preparation.

Keywords

Computer-Assisted Instruction (CAI), International English Language Testing System ( IELTS), Error Analysis (EA)

How to Cite

Chong, K. K. & Ho, A. & Cheung, O. & Leung, E. & Clarke, P., (2014) “AUTOMATED PREDICTION OF THE WRITTEN ERRORS OF TERTIARY LEVEL ESL AND EFL LEARNERS”, Journal of Second Language Acquisition and Teaching 21, p.84-109.

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Authors

Kelvin Ka Yu Chong (University of Arizona)
Allen Ho (Chinese University of Hong Kong)
Olive Cheung (Chinese University of Hong Kong)
Ella Leung (Chinese University of Hong Kong)
Peter Clarke (exida Asia Pacific Pte Ltd)

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Creative Commons Attribution 4.0

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This article has been peer reviewed.

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