Beyond Universal Models: Predicting Trait Emotional Intelligence’s Context-Contingent Effects on EFL Learners’ Attitudes, Motivation, Anxiety, and Engagement

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.

Authors

Institutions

Publication Details

Journal
Education Sciences
Published
2025-09-01
DOI
https://doi.org/10.3390/educsci15091137
Citations
1
Primary Topic
Emotional Intelligence and Performance
Type
article
Field-Weighted Citation Impact
2.44

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Beyond Universal Models: Predicting Trait Emotional Intelligence’s Context-Contingent Effects on EFL Learners’ Attitudes, Motivation, Anxiety, and Engagement

Shaista Rashid, Sadia Malik
1 citations
Education Sciences
Emotional Intelligence and Performance
2.44
article

Beyond Universal Models: Predicting Trait Emotional Intelligence’s Context-Contingent Effects on EFL Learners’ Attitudes, Motivation, Anxiety, and Engagement

Shaista Rashid, Sadia Malik
article en
1 citations

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.

Education SciencesVol. 15(9)
Bahauddin Zakariya University (PK), Prince Sultan University (SA)
Prince Sultan University
Openalex Percentile: Top 10%
Emotional Intelligence and Performance
2.44
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.