Latent Profiles of Chinese Undergraduates’ English Speaking Anxiety and Enjoyment: Associations With Speaking Self‐Efficacy

ABSTRACT This study examined the latent profiles of Chinese undergraduates’ English‐speaking emotions (anxiety and enjoyment) and their association with speaking self‐efficacy. A total of 492 Chinese college students participated in the questionnaire investigation. Latent profile analysis (LPA) revealed three distinct emotional profiles: “Anxiety‐Overpowered Profile,” characterized by high levels of speaking anxiety and low levels of speaking enjoyment; “Emotionally Neutral Profile,” with moderate levels of anxiety and enjoyment; and “Enjoyment‐Filled Profile,” marked by low anxiety and high enjoyment. Subsequent analyses (ANOVA and Welch's tests) revealed that students’ speaking self‐efficacy also varied significantly across the profiles: those in the “Enjoyment‐Filled Profile” showed the highest self‐efficacy, followed by the “Emotionally Neutral Profile,” while those in the “Anxiety‐Overpowered Profile” reported the lowest. Furthermore, multiple regression analyses discovered notable differences in the predictive effects of speaking anxiety and enjoyment on self‐efficacy across the three profiles. These findings confirmed the emotional heterogeneity among learners in the specific field of English speaking. The study also contributed to a more nuanced understanding of the relationship between speaking emotion and self‐efficacy.

Authors

Institutions

Publication Details

Journal
International Journal of Applied Linguistics
Published
2025-12-19
DOI
https://doi.org/10.1111/ijal.70078
Primary Topic
EFL/ESL Teaching and Learning
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Latent Profiles of Chinese Undergraduates’ English Speaking Anxiety and Enjoyment: Associations With Speaking Self‐Efficacy

Xiaoping Chen, Li Cha
International Journal of Applied Linguistics
EFL/ESL Teaching and Learning
article

Latent Profiles of Chinese Undergraduates’ English Speaking Anxiety and Enjoyment: Associations With Speaking Self‐Efficacy

Xiaoping Chen, Li Cha
article en

Abstract

ABSTRACT This study examined the latent profiles of Chinese undergraduates’ English‐speaking emotions (anxiety and enjoyment) and their association with speaking self‐efficacy. A total of 492 Chinese college students participated in the questionnaire investigation. Latent profile analysis (LPA) revealed three distinct emotional profiles: “Anxiety‐Overpowered Profile,” characterized by high levels of speaking anxiety and low levels of speaking enjoyment; “Emotionally Neutral Profile,” with moderate levels of anxiety and enjoyment; and “Enjoyment‐Filled Profile,” marked by low anxiety and high enjoyment. Subsequent analyses (ANOVA and Welch's tests) revealed that students’ speaking self‐efficacy also varied significantly across the profiles: those in the “Enjoyment‐Filled Profile” showed the highest self‐efficacy, followed by the “Emotionally Neutral Profile,” while those in the “Anxiety‐Overpowered Profile” reported the lowest. Furthermore, multiple regression analyses discovered notable differences in the predictive effects of speaking anxiety and enjoyment on self‐efficacy across the three profiles. These findings confirmed the emotional heterogeneity among learners in the specific field of English speaking. The study also contributed to a more nuanced understanding of the relationship between speaking emotion and self‐efficacy.

International Journal of Applied Linguistics
Northeast Normal University (CN)
Openalex Percentile: Top 52%
EFL/ESL Teaching and Learning
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.