The Place of Grammar Instruction in the 21st Century: Exploring Global Perspectives of English Teachers towards the Role of Teaching Grammar in EFL/ESL Classrooms
Bibliographic record
Abstract
This study explores the perspectives held by English teachers around the world regarding the role of teaching grammar in EFL/ESL classrooms and whether the divergence in these perspectives is influenced by variables such as gender, country, seniority, or educational background of the participants. To accomplish these objectives, a comprehensive questionnaire survey from literature comprising 46 items was carried out on a group of 304 participants from 22 countries who were voluntarily involved in this study using the snowball sampling method. Descriptive statistical methods were deployed for accurate data analysis. The findings revealed that the participants had a positive and constructive attitude regarding the importance of grammar instruction and were in unanimous agreement that good grammatical skills enabled the faster acquisition of proficiency in the target language. The participants agreed that grammatical mistakes’ immediate correction was detrimental students’ self-confidence levels and considered unnecessary interruption. These views highly influenced teaching methods and classroom practices. An overwhelming majority preferred an inductive and explicit approach to grammar teaching. It was concluded that while statistical differences in age, gender, and educational background did not influence the participants’ perspectives, seniority and country of origin played a vital role in these beliefs. After due consideration of these findings, a comprehensive discussion of the pedagogical implications and recommendations has been presented in this study.
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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 | 0.006 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".