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Record W4383907986 · doi:10.5430/wjel.v13n7p116

Language Learning Strategies to Improve English Speaking Skills among Vietnamese Students: A Case of Three High Schools in Binh Duong Province, Vietnam

2023· article· en· W4383907986 on OpenAlexvenueno aff
Tran Thanh Du, Hoàng Thị Lệ Quyên

Bibliographic record

VenueWorld Journal of English Language · 2023
Typearticle
Languageen
FieldArts and Humanities
TopicSecond Language Learning and Teaching
Canadian institutionsnot available
Fundersnot available
KeywordsVietnameseHo chi minhPsychologyMathematics educationEnglish languageLinguisticsScale (ratio)Geography

Abstract

fetched live from OpenAlex

The role of language learning strategies (LLS) in second language acquisition has received increased attention across several disciplines in recent years. LLS has been shown to occur in many studies over the years to improve language learning efficiency. The current study endeavors to scrutinize LLS employed by the students at (1) Binh Phu, (2) Vo Minh Duc, and (3) Nguyen Thi Minh Khai high schools and suggests solutions to improve the effectiveness of LLS use. Based on the qualitative and quantitative data, the survey reveals that students’ motivation enormously affected LLS. The findings simultaneously show that students use many LLS to enhance their speaking skills, but the most frequently used ones are cognitive and affective. Significant correlations among types of LLS and the influence of motivation on the choices of LLS are consistent with previous studies. The study’s results are expected to be beneficial to teachers of English and students in terms of narrowing the gap between the students' LLS and their teaching methodologies preferences and sketching out the appropriate strategies to enhance students’ speaking skills. The implications of these findings and the importance of viewing learners holistically are discussed, and recommendations are made for ongoing research.

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)0.002
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.043
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.002
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 head0.008
GPT teacher head0.254
Teacher spread0.247 · 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.

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

Citations3
Published2023
Admission routes1
Has abstractyes

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