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Record W2805808733 · doi:10.5539/ijel.v8n5p142

Exploring the Factors of Foreign Language Anxiety Among Chinese Undergraduate English Majors and Non-English Majors

2018· article· en· W2805808733 on OpenAlexvenueno aff
Mehwish Naudhani, Wu Zhi, Sehrish Naudhani

Bibliographic record

VenueInternational Journal of English Linguistics · 2018
Typearticle
Languageen
FieldArts and Humanities
TopicEFL/ESL Teaching and Learning
Canadian institutionsnot available
Fundersnot available
KeywordsAnxietyPsychologyForeign languageFirst languageForeign language anxietyEnglish languageMathematics educationEnglish as a foreign languageLinguistics

Abstract

fetched live from OpenAlex

The study aims to examine three factors of foreign language anxiety i.e. speaking anxiety, foreign language classroom anxiety and teacher-generated anxiety, among Chinese English majors and non-English majors. The data were analysed to find out which of these factors invoke more anxiety. Research data collection was done via Foreign Language Anxiety Scale. A total number of subjects are 101, including 51 English majors and 50 non-English majors, with Chinese as their mother-tongue and learning English as a second language at university. The results revealed that English majors feel the middle level of foreign language speaking and classroom anxiety while Non-English majors experience high level of foreign language speaking and classroom anxiety. Moreover, both groups felt more anxious when they spoke to the teacher. Keeping in view the results of the study, some follow-up studies are recommended.

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)0.001
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 score0.012
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
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 head0.028
GPT teacher head0.260
Teacher spread0.233 · 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

Citations20
Published2018
Admission routes1
Has abstractyes

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