JOURNAL ARTICLE
Praise Proficiency: Unraveling Student Perceptions of Praise Types in an ESL Classroom.
Published In: Writing & Pedagogy, 2025, v. 16, n. 2. P. 188 1 of 3
Database: Education Source Ultimate 2 of 3
Authored By: Matthews, Kate; Eckstein, Grant; Baker, Wendy 3 of 3
Abstract
This article investigates how ESL (English as a Second Language) students perceive two types of written praise feedback—person praise (e.g., "You are a good writer") and performance praise (e.g., "You used the past tense correctly")—considering variations in language proficiency and cultural background. The study involved 100 ESL students from Romance and Asian language backgrounds, divided into lower and higher proficiency groups, who received both praise types on their own essays and completed surveys and focus groups. Quantitative results showed minimal differences in students' perceptions of person versus performance praise, though focus group discussions revealed a clear preference for performance praise, which students found more specific and motivating. Additionally, lower proficiency students valued praise more and were more likely to consider making changes based on it, while students from Romance language backgrounds generally responded more positively to praise than those from Asian backgrounds. The findings suggest that ESL teachers should tailor praise feedback by emphasizing clear, performance-based comments and consider students' proficiency levels and cultural backgrounds to enhance motivation and avoid confusion.
Additional Information
- Source:Writing & Pedagogy. 2025/12, Vol. 16, Issue 2, p188
- Document Type:Article
- Subject Area:Language and Linguistics
- Publication Date:2025
- ISSN:17565839
- DOI:10.3138/wap-2024-0009
- Accession Number:190496260
- Copyright Statement:Copyright of Writing & Pedagogy is the property of University of Toronto Press and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Looking to go deeper into this topic? Look for more articles on EBSCOhost.