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Record W2157513991 · doi:10.5539/elt.v8n3p46

Chinese University EFL Learners’ Foreign Language Writing Anxiety: Pattern, Effect and Causes

2015· article· en· W2157513991 on OpenAlexvenueno aff
Meihua Liu, Huiliuqian Ni

Bibliographic record

VenueEnglish Language Teaching · 2015
Typearticle
Languageen
FieldArts and Humanities
TopicEFL/ESL Teaching and Learning
Canadian institutionsnot available
FundersTsinghua University
KeywordsPsychologySecond language writingAnxietyApprehensionForeign languageEnglish as a foreign languageForeign language anxietyCommunication apprehensionTest (biology)Language assessmentLanguage proficiencyMathematics educationLinguisticsSecond language

Abstract

fetched live from OpenAlex

This paper reports on the result of a study on Chinese university EFL learners’ foreign language writing anxiety in terms of general pattern, effect and causes. 1174 first-year students answered the 26-item Foreign Language Writing Anxiety Scale (FLWAS) (Young, 1999) and took an English writing test, 18 of whom were invited for semi-structured interviews. The results showed that 1) FLWAS had three principal components—low confidence in English writing (FLWAS1), dislike of English writing (FLWAS2) and English writing apprehension evaluation (FLWAS3), 2) the whole sample, as well as male and female students, were generally confident in and liked English writing, and were not apprehensive of having their English writing evaluated, 3) significant differences existed between male and females students, and among different proficiency groups in all the FLWAS scales, 4) foreign language writing anxiety significantly negatively affected students’ performance in the English writing test, and 5) a number of factors contributed to the students’ foreign language writing anxiety.

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)0.001
metaresearch head score (Gemma)0.003
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 score0.007
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.011
GPT teacher head0.242
Teacher spread0.230 · 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

Citations61
Published2015
Admission routes1
Has abstractyes

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