Retesting the limits of data-driven learning: feedback and error correction

doi:10.1080/09588221.2017.1312462

Retesting the limits of data-driven learning: feedback and error correction

2017-07-26

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Peter Crosthwaite *

DOI：10.1080/09588221.2017.1312462

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An increasing number of studies have looked at the value of corpus-based data-driven learning (DDL) for second language (L2) written error correction, with generally positive results. However, a potential conundrum for language teachers involved in the process is how to provide feedback on students' written production for DDL. The study looks at DDL-mediated error correction across 61 written samples submitted by 32 tertiary students during a series of short DDL courses. Teachers provided feedback on errors present in the samples, and students highlighted corrections made with or without the corpus. The results suggest that students used corpora to correct errors of word choice, word form, collocations and phrasing, but were less likely to use corpora to correct errors of deletion or morphosyntax. When the corpus was used, students were likely to successfully correct errors of collocation but were less successful for errors of morphosyntax. Post-course questionnaires suggested that perception of the usefulness of DDL for grammar learning was less than that for vocabulary and the learning of phrases, and that time and effort spent on analyzing concordance data and understanding the teacher's feedback on their writing were perceived as difficulties. To explore these findings further, a qualitative analysis of the feedback teachers provided suggests significant difficulties devising appropriate feedback that promotes autonomous, inductive language acquisition for all error types on the one hand, and at the same time does not eliminate the need for corpus consultation nor is too vague for students to formulate appropriate corpus queries. This study is therefore an initial yet important step in identifying the type of errors that teachers can address in a timely manner with focused feedback leading to corpus consultation, and how such feedback affects the success of this consultation.

Keyword:

Data-driven learning

English for academic purposes

corpus linguistics

error correction

written corrective feedback

Retesting the limits of data-driven learning: feedback and error correction

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