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Record W4307866538 · doi:10.5539/elt.v15n11p78

Relationship Between Language Anxiety and English Speaking Performance

2022· article· en· W4307866538 on OpenAlexvenueno aff
I‐Ju Chen, Kai-Ru Cheng, Chieh-Hsiang Chuang

Bibliographic record

VenueEnglish Language Teaching · 2022
Typearticle
Languageen
FieldArts and Humanities
TopicEFL/ESL Teaching and Learning
Canadian institutionsnot available
Fundersnot available
KeywordsPsychologyAnxietyLanguage assessmentDescriptive statisticsForeign languageForeign language anxietyLanguage proficiencyTest (biology)Pearson product-moment correlation coefficientEnglish languageEnglish as a foreign languageTest anxietyMathematics education

Abstract

fetched live from OpenAlex

This study investigated the relationship between language language anxiety and English speaking performance among Taiwanese college students. The participants were 59 students attending a one-semester course entitled English Speaking Communication at a university in central Taiwan. They underwent computer-based oral proficiency tests and completed the Foreign Language Classroom Anxiety Scale. Data were analyzed using descriptive statistics and Pearson product-moment correlation. The English majors experienced moderate levels of anxiety when taking the computer-based speaking test, and a negative but nonsignificant correlation was discovered between foreign language anxiety and speaking performance on the computer-based exam. Pedagogical suggestions for second-language educators are presented herein.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.022
GPT teacher head0.246
Teacher spread0.224 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations7
Published2022
Admission routes1
Has abstractyes

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