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Record W2488030667 · doi:10.5539/elt.v9n9p52

Learning Strategies in Alleviating English Writing Anxiety for English Language Learners (ELLs) with Limited English Proficiency (LEP)

2016· article· en· W2488030667 on OpenAlexvenueno aff
Chia-Pei Wu, Huey-Ju Lin

Bibliographic record

VenueEnglish Language Teaching · 2016
Typearticle
Languageen
FieldArts and Humanities
TopicEFL/ESL Teaching and Learning
Canadian institutionsnot available
Fundersnot available
KeywordsEllPsychologyAnxietyLanguage proficiencyLimited English proficiencyEnglish languageLanguage learning strategiesSecond language writingMathematics educationMetacognitionDevelopmental psychologyTeaching methodLinguisticsVocabulary developmentSecond languageCognition

Abstract

fetched live from OpenAlex

This study utilized the Oxford Strategy Inventory for Language Learning (SILL) and an English writing anxiety scale to examine the relationship between learning strategies and English writing anxiety in 102 university-level English language learners (ELLs) with Limited English Proficiency (LEP) in a university in Taiwan. Kruskal Wallis Test results revealed no significant association between learning strategies and English writing anxiety. The common learning strategies utilized by participants were compensation, social, memory and mixed strategies. The interview data indicated that ELLs suffered considerably from writing anxiety. Coping strategies of highly anxious ELL of each learning strategy group is also reported. However, further studies of larger populations and comparison of different ethnic groups as well as quantitative statistics analyses are needed.

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)0.002
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.012
GPT teacher head0.243
Teacher spread0.231 · 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

Citations13
Published2016
Admission routes1
Has abstractyes

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