Model‐based feedback for L2 writing revision: The role of vocabulary size and language aptitude

Abstract

Although the utility of model‐based feedback is well documented, little is known about which factors modulate its effectiveness. The current study investigated the extent to which learner differences in receptive and productive vocabulary sizes, as well as differences in their language aptitude (measured by the LLAMA test), mediated the effects of using models as a written corrective feedback (WCF) tool. Sixty Korean learners of English (ages 18–21) were divided into an experimental and a control group. They all completed an argumentative writing task. Before revising their texts, the experimental group received model texts to compare their first draft, while the control group self‐corrected their errors without a model. Subsequently, all 60 participants took the LLAMA and vocabulary size tests. The model group's revisions showed significant improvements in vocabulary, content, and organization compared to the control group. The results also showed that learners’ receptive and productive vocabulary sizes significantly predicted the effectiveness of model‐based feedback.

Adopting ChatGPT as a writing buddy in the advanced L2 writing class

“…In the past decades, modelling has received greater attention from L2 scholars (e.g., Hanaoka, 2007;Kang, 2023;García Mayo & Labandibar, 2017;Roothooft et al, 2022, Wu et al, 2023. Accordingly, writers use the models predominantly to revise vocabulary (e.g., Cánovas Guirao et al, 2015;Hanaoka, 2007;García Mayo & Labandibar, 2017;Wu et al, 2023) and, only to a lesser extent manage to identify content issues.…”