Teachers’ Perception on the Implementation of Dual Language Programme (DLP) in Urban Schools
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".