The Influence of Teaching Experience, School Location and Academic Background on Teachers’ Beliefs in Teaching Grammar
Bibliographic record
Abstract
The beliefs that teachers hold regarding teaching will have a strong impact on the kinds of decisions that theymake in their classrooms. The type of materials, activities and instruction they will use in their lessons will beguided by these beliefs. At the same time, without having conviction in their beliefs about how students learn, itis difficult to imagine teachers being enthusiastic and effective in their teaching regardless of the approach theytake. In teaching English grammar to second language learners, teachers often subscribe to their own set ofpersonal beliefs that have been formed, most likely through their experience as well training. This applies as wellto the teaching of grammar which has had various competing points of view in terms of how it should be taught.This paper examines teachers’ beliefs using data collected from a survey administered to 345 English languageteachers in secondary schools in two states in Malaysia. A self-developed instrument was used to investigate fourdifferent emphases in the teaching of grammar in the classroom – input, explicit L2 knowledge, student outputand error correction – as proposed by Ellis (1998). The data was analysed according to how teaching experience,school location, and academic background can influence teachers’ views towards the importance of each of theseemphasis in teaching grammar. The results indicate a number of interesting points which can be of helpespecially in teacher training and professional development.
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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 | 0.010 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".