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Record W2160336906 · doi:10.5539/elt.v6n1p153

Language Learning Strategies of English for Specific Purposes Students at a Public University in Malaysia

2012· article· en· W2160336906 on OpenAlexvenueno aff
Mohamed Ismail Ahamad Shah, Yusof Ismail, AinonJariah Mohamed, Zaleha Esa

Bibliographic record

VenueEnglish Language Teaching · 2012
Typearticle
Languageen
FieldArts and Humanities
TopicEFL/ESL Teaching and Learning
Canadian institutionsnot available
Fundersnot available
KeywordsPsychologyLanguage learning strategiesEnglish languageMathematics educationPublic universityEnglish for academic purposesPedagogyMedical educationMetacognitionCognition

Abstract

fetched live from OpenAlex

Studies on strategy research have shown the usefulness and importance of language learning strategies (LLS) for ESL and EFL learners. However, research on content-based learners in relation to English for Academic Purposes (EAP) and English for Occupational Purposes (EOP) has yet to be undertaken. This study, therefore, investigated the learning strategies of students at a public English medium university in Malaysia. The study was mainly motivated by concerns about the standards of English of graduates of Malaysian universities. These concerns have also been expressed by the university authorities. The purpose of the research was to investigate the patterns of LLS as reported by the students according to gender, courses, and undergraduate programmes. A total of 312 students from three degree programmes participated in this study. Their learning strategies were investigated based on the Strategy Inventory for Language Learning (SILL) (Oxford, 1990). The findings of the study indicated that the students from the different degree programmes differed in the use of LLS. However, there was no statistically significant relationship between LLS and gender.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.192
Threshold uncertainty score0.934

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
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 head0.022
GPT teacher head0.255
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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations15
Published2012
Admission routes1
Has abstractyes

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