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Record W2907880979 · doi:10.5539/ass.v15n1p24

Teachers’ Perception on the Implementation of Dual Language Programme (DLP) in Urban Schools

2018· article· en· W2907880979 on OpenAlexvenueno aff
Nadiah Binti Has Bullah, Melor Md Yunus

Bibliographic record

VenueAsian Social Science · 2018
Typearticle
Languageen
FieldArts and Humanities
TopicEFL/ESL Teaching and Learning
Canadian institutionsnot available
Fundersnot available
KeywordsMathematics educationPerceptionLingua francaChristian ministrySession (web analytics)PsychologyPedagogyMedical educationComputer sciencePolitical scienceHumanitiesMedicine

Abstract

fetched live from OpenAlex

Today, English Language has become the language of communication in every area which includes economics, politics, technologies as well as education. It also has been placed as a second language in Malaysia due to its importance. Dual language programme (DLP) has been introduced and implemented in selected school since 2016 as an initiative programme to give the pupils the opportunity to use English Language in learning Science and Mathematics. It aimed to produce proficient pupils in the international lingua franca language. Thus, this study was conducted to examine the teachers’ perceptions towards the implementation of DLP and to identify the factors that affect the implementation of DLP in urban schools. English, Science and Mathematics teachers from urban schools that implemented DLP were selected as respondents for this study. In order to achieve the objectives of this study, a set of questionnaire was developed and interview session were conducted to gather their point of view. Data was analysed using descriptive analysis. The results from the questionnaires and interview were presented in tables and figures and showed that the teachers have positive perceptions towards the implementation of DLP. However, there were some challenges faced by the teachers such as lack of teaching resources and facilities. This study’s result aspires the ministry and DLP teachers to discover the possible solution to improve the implementing the programme.

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)0.000
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 score0.185
Threshold uncertainty score0.801

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.037
GPT teacher head0.333
Teacher spread0.295 · 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

Citations14
Published2018
Admission routes1
Has abstractyes

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