Sketching the landscape of corrective feedback by bibliometric analysis and structural topic modeling

Abstract

Corrective feedback (CF) has been playing an important role in language teaching. Even though previous reviews focused on written or oral corrective feedback, little attention has been drawn to provide a panoramic review of the whole CF field. This study aims to sketch the landscape of CF research over the past two decades (2000-2022) and identify latent topics of the CF field. A total of 1106 CF-related articles were analyzed using bibliometric analysis and structural topic modeling. The most influential journals, references, countries, and authors in CF were identified by bibliometric analysis. Eighteen important topics in CF were discovered by structural topic modeling, among which the most representative topics included eight student-related topics, four teacher-related topics, and four technology-related topics. The findings showed that among these eighteen topics, implicit and explicit CF, teachers’ beliefs in CF and uptake of recast accounted for the largest proportion. Meanwhile, the topic trends indicated that more attention should be paid to peer feedback, automated writing evaluation of feedback, assessment literacy and student engagement in the future. More importantly, this study clarifies the relationship among teachers, students and technology in the CF field and constructs a conceptual framework in CF. This study contributes to pointing out potential directions for further CF studies and provides implications for deepening the understanding of CF in the language teaching field.