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Record W2128233387 · doi:10.5539/elt.v7n5p59

Use of English Vocabulary Learning Strategies by Thai Tertiary-Level Students in Relation to Fields of Study and Language-Learning Experiences

2014· article· en· W2128233387 on OpenAlexvenueno aff
Nathaya Boonkongsaen, Channarong Intaraprasert

Bibliographic record

VenueEnglish Language Teaching · 2014
Typearticle
Languageen
FieldPsychology
TopicSecond Language Acquisition and Learning
Canadian institutionsnot available
Fundersnot available
KeywordsPsychologyTertiary levelMathematics educationDescriptive statisticsVocabularyRelation (database)Vocabulary learningEnglish languageLanguage learning strategiesData collectionMetacognitionCognitionLinguisticsStatisticsSocial scienceComputer scienceSociologyMathematics

Abstract

fetched live from OpenAlex

The present study was intended to examine the effects of 1) fields of study (arts, business and science-oriented); and 2) language-learning experiences (whether limited or non-limited to formal classroom instructions) on the use of VLSs among Thai tertiary-level students. The participants were 905 Thai EFL students studying in the Northeast of Thailand. The VLS questionnaire was employed for data collection. Descriptive statistics, an Analysis of Variance (ANOVA) and the chi-square tests were performed for data analysis. The results revealed that fields of study and prior language-learning experiences affected the students’ overall VLS use, use of VLSs by the category and the individual strategy levels. The variation patterns of students’ VLS use were found in relation to the two variables.

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)0.003
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 score0.003
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
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 head0.015
GPT teacher head0.312
Teacher spread0.297 · 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

Citations10
Published2014
Admission routes1
Has abstractyes

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