Mathematics and science teachers’ beliefs and practices regarding the teaching of language in content learning
Bibliographic record
Abstract
This article presents data from a study examining secondary mathematics teachers’ and science teachers’ implementation of a language of instruction policy in Malaysia, which made English the medium for mathematics and science instruction. It explores the beliefs of math, science and language teachers, and how these beliefs influence their pedagogical practices in content-based language instruction classrooms. The study uses a mixed-methods approach for data collection and data analysis. Data is analysed using perspectives from content-based language teaching (CBLT) and from research on mathematics and science instruction for English language learners (ELLs). The results indicate that teachers’ beliefs about their respective roles as only content teachers or only language teachers limit students’ language learning opportunities. Factors such as curricular requirements, exam pressure and time constraints also shape classroom interactions, and have implications for student learning as well. The findings reveal the lack of collaboration between content and language teachers, and the need for sustained professional development concerning content and language integration for both groups of teachers. This study extends work on content-based language teaching to the previously unexamined Malaysian context. Its findings contribute to the ongoing work of improving instructional practices in content-based classrooms to integrate and maximize content and language learning for English language learners.
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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 | 0.004 | 0.013 |
| 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 | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".