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Record W2160396835 · doi:10.5539/ass.v10n3p174

Revisiting English Language Learning among Malaysian Children

2014· article· en· W2160396835 on OpenAlexvenueno aff
Hamidah Yamat, R. Fisher, Sarah Rich

Bibliographic record

VenueAsian Social Science · 2014
Typearticle
Languageen
FieldArts and Humanities
TopicEFL/ESL Teaching and Learning
Canadian institutionsnot available
FundersUniversiti Kebangsaan Malaysia
KeywordsObstacleEnglish languageEthnographyLanguage acquisitionLanguage policyPsychologyPsychological interventionGrounded theoryPoint (geometry)Mathematics educationPedagogySociologyQualitative researchPolitical scienceSocial science

Abstract

fetched live from OpenAlex

Despite learning English language for six years at elementary and five years at secondary levels, Malaysian students’ English language competency has always been the obstacle in securing success at university level as well as in job opportunities. Hence, various interventions have been taken in the teaching and learning process as well as changes in language policy. This paper calls for a revisit on Malaysia’s policy on teaching English English at primary schools. It discusses the findings of English language acquisition as experienced by Azlan, Hazwan and Aida’s (pseudonyms), aged six, and explored through an ethnographic case study. The children’s, their mother’s and teacher’s voices were gathered through interviews. The children’s behaviours in and outside of school and at home were also captured through observations. A grounded theory data analysis approach was employed in analysing the data. Findings illuminated that for these children, the second language was acquired through play and use; and that developing children’s confidence should be the starting point. The implication of this finding is discussed in the light of the English language policy for teaching English to Malaysian primary school children.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.585
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0030.001
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.233
Teacher spread0.226 · 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

Citations38
Published2014
Admission routes1
Has abstractyes

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