Guiding the Future: The Role of L2 Self‐Guides in Predicting Pre‐Service Language Teachers' Professional Commitment.

  • Published In: International Journal of Applied Linguistics, 2025, v. 35, n. 4. P. 2125 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Zhang, Yiran; Chen, Qi; Peng, Yue 3 of 3

Abstract

This study explores the relationship between L2 self‐guides and the affective, continuance, and normative professional commitment of 98 pre‐service English teachers in China. The multiple regression results reveal that the ought L2 self/own significantly predicted each of the three dimensions of professional commitment, indicating that internalized responsibilities that develop during language learning play a crucial role in fostering persistence, obligation, and emotional attachment to teaching in this context. The ought L2 self/other negatively predicted affective commitment, suggesting that external pressures may hinder emotional alignment with the profession. The ideal L2 selves did not significantly predict commitment, implying that the participants' professional dedication may rely more on perceived obligations than aspirational goals. The findings highlight the enduring influence of language learning experiences in shaping pre‐service language teachers' professional dedication and yield implications for understanding the developmental pathways of professional commitment in language teacher education. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:International Journal of Applied Linguistics. 2025/11, Vol. 35, Issue 4, p2125
  • Document Type:Article
  • Subject Area:Language and Linguistics
  • Publication Date:2025
  • ISSN:0802-6106
  • DOI:10.1111/ijal.12756
  • Accession Number:189063640
  • Copyright Statement:Copyright of International Journal of Applied Linguistics is the property of Wiley-Blackwell 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.)

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