Bibliographic record
Abstract
While much has been written about teacher cognition in grammar teaching, research investigating non-native English as a Foreign Language (EFL) teacher cognition in grammar teaching remains limited. This study intends to investigate non-native EFL teacher cognition in teaching grammar to university students in the Saudi Arabian context. More specifically, the study examins the interplay between these teachers’ beliefs and practices in grammar teaching across mother tongue and gender. For this purpose, the study used mixed methods design, and employed a five-point Lickert scale questionnaire triangulated by a structured classroom observations checklist. Sixty teachers were selected for questionnaire, based on stratified random sampling; while eight teachers were observed multiple times. Teachers’ selection for observations was based on purposive sampling. Both types of data were analysed statistically using Statistical Package for Social Sciences (SPSS). Descriptive analyses and independent-samples t-tests were employed. The results of independent samples t-tests indicated that there were no statistically significant differences in the beliefs of teachers across mother tongue and gender. The main finding of the study, revealed through descriptive analysis of the data, is that beliefs and practices of teachers across mother tongue and gender were at odds resulting into weak teacher cognition. The study suggests pedagogical implications for improved teacher cognition and hence, better grammar teaching in the Saudi Arabian context.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".