Using learner corpus data for grammatical accuracy development in written productions

Abstract

This study investigates the differential effect of various noticing activities on grammatical accuracy development in EFL learners’ written productions. We focus on different types of noticing activities based on an error-tagged learner corpus and report on effective practical experiments involving learner corpus data. A pretest/posttest quasi-experimental design is used with three experimental groups (receiving different treatments) and one control group. Error frequencies, at both group and individual levels, and proportions of learners producing errors on three specific error types (articles, verb tense, verb agreement) are compared. Our results suggest that accuracy in the use of articles and verb agreement could be more easily fostered through the comparison of learner output with native data (the BNC, in our case). As for verb tenses, the impact of a more traditional form of corrective feedback seems greater while the use of online machine translation tools does not seem to foster much accuracy development.