THE USE OF GRAMMAR TRANSLATION METHOD IN ENGLISH LEARNING TO THE SUB-DISTRICTS’ JUNIOR HIGH SCHOOLS IN TABANAN REGENCY
Bibliographic record
Abstract
Grammar Translation Method is regarded as the old method that still used particularly at the school in sub-districts area. This research purposed to describe the use of Grammar Translation Method in English learning to the sub-districts junior high schools in Tabanan regency. The subjects of this research were three English teachers and 124 students. The data collection techniques used in this research were observation, interview, and documentation. Qualitative data analysis was applied to analyze the data. In accordance with data analysis, it was obtained that (1) Grammar Translation Method (GTM) was used in learning process includes the phases of observing, questioning, collecting data, associating, communicating, (2) Grammar Translation Method was used to increase students’ knowledge and skills in reading and writing. (3) The difficulties faced by the students were utilizing the appropriate vocabulary and structuring sentences. The conclusion of this research is the use of Grammar Translation Method is still required to increase students’ capability and skills, especially in reading and writing.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".