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Abstract

This study investigates the influence of Trait Emotional Intelligence (TEI) on affective dimensions of English language learning among 515 Pakistani EFL learners, addressing a key gap in Global South research. Using bootstrapped multiple regression and culturally adapted instruments (Cronbach’s α = 0.724–0.857), findings reveal that in Pakistan’s exam-driven, teacher-centered classrooms, well-being significantly enhances attitudes (β = 0.172, p < 0.001), motivation (β = 0.219, p = 0.002), and engagement (β = 0.179, p < 0.001). Emotionality, however, increases anxiety (β = 0.192, p < 0.001) and lowers engagement (β = −0.092, p = 0.025), contradicting global models due to punitive error correction. Sociability shows no significant effect (attitudes: β = 0.038, p = 0.366; engagement: β = 0.019, p = 0.613), reflecting limited peer interaction in hierarchical classrooms. Notably, an emergent auxiliary facet—contextual adaptability—strongly predicts motivation (β = 0.269, p < 0.001) and anxiety (β = 0.109, p = 0.020), highlighting the role of competencies like Urdu–English code-switching. These results call for a Contextually Stratified TEI Framework, emphasizing that while well-being is universal, other TEI dimensions are context-dependent. Implications urge educators to foster well-being, reframe emotionality as a risk-detection skill, and promote adaptability to local linguistic realities.

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1009240
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Title
Beyond Universal Models: Predicting Trait Emotional Intelligence’s Context-Contingent Effects on EFL Learners’ Attitudes, Motivation, Anxiety, and Engagement
Author
Rashid Shaista 1   VIAFID ORCID Logo  ; Malik Sadia 2   VIAFID ORCID Logo 

 Linguistics and Translation Department, College of Sciences & Humanities, Prince Sultan University, Riyadh 12435, Saudi Arabia; [email protected] 
 Department of English, Bahauddin Zakariya University, Multan 60000, Pakistan 
Publication title
Volume
15
Issue
9
First page
1137
Number of pages
22
Publication year
2025
Publication date
2025
Publisher
MDPI AG
Place of publication
Basel
Country of publication
Switzerland
Publication subject
e-ISSN
22277102
Source type
Scholarly Journal
Language of publication
English
Document type
Journal Article
Publication history
 
 
Online publication date
2025-09-01
Milestone dates
2025-08-09 (Received); 2025-08-28 (Accepted)
Publication history
 
 
   First posting date
01 Sep 2025
ProQuest document ID
3254509382
Document URL
https://www.proquest.com/scholarly-journals/beyond-universal-models-predicting-trait/docview/3254509382/se-2?accountid=208611
Copyright
© 2025 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.
Last updated
2025-11-07
Database
ProQuest One Academic