Ways to Minimize the Influence of L1 (Bahasa Melayu) Into L2 (English) Tenses Writing among Form 2 Secondary School Students in Ipoh
Bibliographic record
Abstract
Writing in English language is one of the most challenging skills faced by English language learners in Malaysia especially if they do not have a good proficiency in the language. This study seeks to identify ways to minimize the influence of L1 (Bahasa Melayu) into L2 (English) tenses writing. The data collection instrument was interview. To execute the study 8 students were interviewed from a public school in Ipoh. This study also discovered that the majority of the methods recommended by the students were cognitive in nature such as listening to songs, and using PowerPoint slides were the most popularly mentioned in the interview session. This study will help educators, students, curriculum coordinators, and decision-makers create the most engaging curriculum possible using effective teaching methods, especially for English language beginners.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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; both teacher heads agree on what is shown here.
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".