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Record W3037107120 · doi:10.5539/ells.v10n3p31

Exploring the Relationship Between Foreign Language Anxiety, Gender, Years of Learning English and Learners’ Oral English Achievement Amongst Chinese College Students

2020· article· en· W3037107120 on OpenAlexvenueno aff
Hualan Tan, Zhilong Xie

Bibliographic record

VenueEnglish Language and Literature Studies · 2020
Typearticle
Languageen
FieldArts and Humanities
TopicEFL/ESL Teaching and Learning
Canadian institutionsnot available
Fundersnot available
KeywordsPsychologyForeign languageFluencyAnxietyCurriculumPedagogyMathematics education

Abstract

fetched live from OpenAlex

English serves as a bridge of communication for the people from all over the world as it plays an increasingly crucial role in the process of globalization. In accordance with English curriculum standards issued by the Ministry of Education in 2011, the ultimate goal of English language discipline is to communicate. But over these years, China’s English education has been difficult to get out of the dilemma of “Dumb English”. When facing the real oral communication situations, students are still too nervous to speak with a great deal of fluency and accuracy. Therefore, the present study aims to explore the relationship between English language anxiety, gender, years of English learning and final oral English achievement by inviting 41 English major freshmen of foreign language departments of Nanchang Business College. For this purpose, this study adopts a reliable Foreign Language Classroom Anxiety Scale developed by Horwitz and Cope (1986) to measure students’ anxiety. The results reveal that anxiety levels between males and females are similar; there is also no significant difference among years of learning English; however, a significantly negative correlation between college students’ foreign language anxiety and their oral English learning achievement was found.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.193
Threshold uncertainty score0.861

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.071
GPT teacher head0.290
Teacher spread0.219 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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

Citations4
Published2020
Admission routes1
Has abstractyes

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