English for Teaching and Learning of Science and Mathematics in Malaysian Schools: A Comparative Study on Perception between Different Ethnics
Bibliographic record
Abstract
This paper reports a study on the perception of the people toward the teaching and learning of Science and Mathematics using English. Altogether, 641 respondents obtained randomly from all over Malaysia participated in the study. The respondents, male and female from the age of 20 to 55 were given a set of questionnaire, containing statements on various issues of Science and Mathematics. Each statement is accompanied with five choices of responses in the form of Likert type scale ranging from 1. Strongly disagree, 2. Disagree, 3. Not sure, 4. Agree, and 5. Strongly agree. One of the statement of the questionnaire analysed was “It is easier to learn science and mathematics in English”. The raw data was analysed using the Statistical Package for the Social Sciences (SPSS) and also ANOVA. The result of the study shows that 45.7% of the respondents rejected the statement, 24.4% accepted the statement and 29.3% were not sure. There is significant difference of means between the respondents with academic background in science compared to respondents with non-science academic background.
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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 | 0.002 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".