Transforming Foreign Language Grammar Classes through Teacher Training: An Experience from Nepal
Bibliographic record
Abstract
reflection of the teacher training output in the real classroom situation. English teachers commonly blame on theunfavourable environment as the main obstacle to the successful classroom application of their knowledge and skillsneeded for teaching English as a foreign language (EFL) gained from professional development programmes. Thepre-training observations of a secondary level EFL teacher's four classes made the basis of a case study for thisresearch. Stemming from the case study, three techniques were used as a process of action research: i) theneed-based refresher training (along with other participants) as an intervention ii) the post-training class observations(in a demonstration class), and iii) informal post-class talks. Thus, this study was an attempt to examine throughaction research germinating from a case study, whether (and to what degree), the output of the Teachers' ProfessionalDevelopment refresher training (TPD refresher) would be transferred to the actual classroom situation. Thepre-training and post-training observations were compared and contrasted to reach the conclusion. The resultssuggest that the output would be reflected to a large degree in the classroom provided that the training is need-based.Thus, it was concluded that if (foreign) (Note 1) language teachers are properly equipped with professionalknowledge and skills through need-based training, (foreign) language classes are very likely to be transformed asdesired.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".