A Contrastive Study of Grammar Translation Method and Communicative Approach in Teaching English Grammar
Bibliographic record
Abstract
The Grammar Translation Method and the Communicative Approach have both played important roles in grammar teaching. Which is better, the Grammar Translation Method or the Communicative Approach? This paper aims to compare the controllability and feasibility of these two approaches and find out which one is more suitable for grammar teaching in Taiwan. Two classes were selected and taught by the Grammar Translation Method and the Communicative Approach respectively. The college admission test showed that they share a similar level of the overall English proficiency before the intervention. The pre-test demonstrated that there wasn’t any distinction between the two classes in their grammatical competence. The post-test embodied that there was significant difference in their grammatical competence between the two classes. The scores of the students in the Experimental Class were higher than that in the Control Class. The result showed that grammar teaching in the framework of the Grammar Translation Method is better than the Communicative Approach. Nevertheless, the Communicative Approach emphasizes fluency and the Grammar Translation Method is concerned with accuracy. Fluency and accuracy are the target for English learning. So the best way to improve the situation is to combine both methods in teaching English Grammar.
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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 | 0.010 | 0.022 |
| 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 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".