JOURNAL ARTICLE
Multicultural content in English language teaching textbooks: The case of the Vision and Top Notch series.
Published In: Journal of Applied Linguistics & Professional Practice, 2023, v. 17, n. 1. P. 73 1 of 3
Database: Communication Source 2 of 3
Authored By: Tirnaz, Mohammad Hossein; Moghaddam, Mostafa Morady 3 of 3
Abstract
This article examines and compares the multicultural content of two widely used English Language Teaching (ELT) textbook series in Iran: the locally developed Vision series and the internationally published American ESL series Top Notch. Using interpretive content analysis based on Gómez Rodríguez's framework distinguishing surface culture (observable elements) and deep culture (underlying sociocultural norms and values), the study finds that the Vision series presents limited cultural elements focused mainly on Iranian (local) culture and lacks sufficient representation of international and deep cultural content. In contrast, the Top Notch series offers a broader and more diverse range of international cultural elements at both surface and deep levels, thus providing greater potential for developing learners' intercultural communicative competence (ICC). Both series, however, predominantly emphasize surface culture, indicating a need for enhanced integration of deep cultural content to better support multicultural education and intercultural communication in ELT contexts.
Additional Information
- Source:Journal of Applied Linguistics & Professional Practice. 2023/01, Vol. 17, Issue 1, p73
- Document Type:Article
- Subject Area:Language and Linguistics
- Publication Date:2023
- ISSN:2040-3658
- DOI:10.1558/jalpp.20434
- Accession Number:166096242
- Copyright Statement:Copyright of Journal of Applied Linguistics & Professional Practice 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.