How error types affect the accuracy of L2 error correction with corpus use

Abstract

This study reports classroom research on corpus use in second-language (L2) error correction to explore how information from corpus data influences L2 writing. As the error types for which corpus consultation is helpful remain uncertain, this study examined the error types for which data-driven learning (DDL) provides good error correction in L2 writing to maximize the benefits of corpus use in L2 class instruction. First, 55 undergraduate students from a university in Tokyo wrote an essay in 25 min without access to reference resources and received teacher or peer feedback on their errors. They then performed revision tasks for 15 min with either use or non-use of reference resources (corpus or dictionary). This procedure was carried out 9–11 times during the term. Error analysis found that error types affected how frequently and accurately learners corrected their errors using corpus data. Corpus use allowed easy access to the exact target phrases and frequency information of co-occurring words, which especially helped participants correct omission errors, and corpus consultation to correct omission errors increased over time. Teachers should consider error types so that DDL can promote accurate error correction in L2 writing and serve as a practical option in L2 classrooms.