The effects of teacher, peer and self-feedback on error correction with corpus use

Abstract

The strengths of corpora in language learning have been stated, while not many studies have explored the effects of feedback on error correction in the settings of data-driven learning (DDL), which is an approach where learners use corpora to learn language patterns inductively. Therefore, this study examines the effects of feedback on second language (L2) error correction with corpus use. The author hypothesizes that seeing many example sentences of the target word(s) with corpus use is useful in correcting L2 errors and that different sources of feedback have different effects on error correction. To test the hypotheses, the effects of teacher feedback on 55 participants’ error correction with use of the Corpus of Contemporary American English (COCA) were compared with those of peer feedback along with those of self-feedback. The results show that teacher feedback especially worked well for correcting omission errors and agreement errors. The strength of teacher feedback was identifying correctable errors. The results suggest that efficient corpus use for error correction requires teachers to consider appropriate combinations of feedback and error types (e.g., teacher feedback for omission errors and agreement errors).