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Record W2143215061 · doi:10.5539/elt.v6n7p139

Code-Switching in English as a Foreign Language Classroom: Teachers’ Attitudes

2013· article· en· W2143215061 on OpenAlexvenueno aff
Engku Haliza Engku Ibrahim, Mohamed Ismail Ahamad Shah, Najwa Tgk. Armia

Bibliographic record

VenueEnglish Language Teaching · 2013
Typearticle
Languageen
FieldSocial Sciences
TopicMultilingual Education and Policy
Canadian institutionsnot available
FundersUniversiti Sains Malaysia
KeywordsCode-switchingMalayForeign languageCode (set theory)PsychologyMathematics educationEnglish as a foreign languageLinguisticsPhenomenonLanguage assessmentComputer scienceProgramming languagePhysics

Abstract

fetched live from OpenAlex

Code-switching has always been an intriguing phenomenon to sociolinguists. While the general attitude to it seems negative, people seem to code-switch quite frequently. Teachers of English as a foreign language too frequently claim that they do not like to code-switch in the language classroom for various reasons – many are of the opinion that only the target language should be used in the classroom. This study looks at the teachers’ attitudes towards code-switching in teaching English as a foreign language to Malay students at one of the local universities in Malaysia. Data was collected through observations, questionnaires and interviews. Each teacher was observed, their language use were recorded, transcribed and then analyzed using the functions proposed by Gumperz (1982). The results of the study showed that teachers do code-switch in the language classroom, despite their claim that they do not. Analysis of the data showed that, in most cases, code switching by teachers was done to serve pedagogical purposes.

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)0.010
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.341
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
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 head0.025
GPT teacher head0.392
Teacher spread0.366 · 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

Citations44
Published2013
Admission routes1
Has abstractyes

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