The Development of English Grammar Learning Skills by Using Explicit Teaching Method of University Students: The Case Study of Thai University Students
Bibliographic record
Abstract
In Thailand, the traditional teaching approach used for English language classes has been to explicitly teach English grammar, but in recent years the strategy has begun to shift towards the implicit teaching of language via communicative techniques. Within academic circles, there has been minimal agreement on the suitability of approaches used. Today, the question of whether it is better to use an implicit or explicit approach remains open to debate. In this study, an explicit approach is used to teach English grammar to learners in the English Program at the Faculty of Education, Lampang Rajabhat University. These learners have experienced implicit teaching during their own learning of English grammar, but upon graduation will have to use explicit methods when they become teachers of English grammar themselves. Initially, it can be observed that their knowledge of English grammar is weak. Also, they may lack the pedagogical skills to teach English grammar explicitly. The target group in this study was 74 Year 1 students. This study involved multiple steps, namely content analysis, a pre-test, explicit English grammar tutoring, a post-test and data analysis. Test results were statistically analysed for percentages, means and standard deviations. Pre- and post-test scores revealed a difference of 360, or 12.16%, with the mean of 4.87 (SD = 9.96). It can be seen that English grammar learning skills of the target group can be developed by using the explicit teaching method and this can support pre-service English teachers to teach grammar more effectively.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".