Bibliographic record
Abstract
Grammar teaching has been a long tradition in EFL instruction in various parts of the world and Saudi Arabia is no exception to this. However, various approaches to teaching grammar have emerged over a period of time. For this, professional development (PD) programs are designed to meet the EFL teachers’ needs by enabling them to use a range of approaches and techniques. To do this successfully, professional needs analysis of teachers is essential. The present study investigates the beliefs of teachers regarding the use of various teaching approaches for grammar teaching and their need for professional development (PD). Questionnaire survey was conducted among 50 randomly chosen EFL teachers at a public sector university. The results showed that EFL teachers deem grammar as a foundational framework for teaching English as a foreign language. Furthermore, grammar is thought to be a major factor in developing accuracy and correct use of EFL. Moreover, the teachers have the theoretical knowledge of various grammar teaching methods using TBL, PBL and CLT. However, they need to develop practical skills for grammar instruction. Thus, the study recommends that the universities in Saudi Arabia need to arrange regular PD programs so that the EFL teachers with modern methods to teach English grammar successfully.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".