The Relationship between Teachers’ Beliefs of Grammar Instruction and Classroom Practices in the Saudi Context
Bibliographic record
Abstract
Teacher cognition (Borg, 2015) of grammar instruction is a relatively new phenomenon that has yet to be explored in the Saudi context. While many studies have focused on the teaching of grammar in general (Ellis, 2006; Corzo, 2013; Braine, 2014), further research needs to be done - particularly when it comes to understanding teachers’ beliefs of grammar and grammar instruction as well as their practices in the classroom. This case study investigates the relationship between teachers’ beliefs of grammar and grammar instruction and their instructional practices. In the first stage, a sample of 30 teaching faculty members at the English Language Institute (ELI) at the University of Jeddah (UJ), in Saudi Arabia completed a survey discussing their beliefs related to grammar instruction. In the second stage, ten of these teachers were observed in classroom in order to explore the relationship between their beliefs and practices. In the third and final stage, open-ended questions were distributed to the teachers after the observations to better understand the factors that influence their beliefs. The findings reveal that teachers’ beliefs are indeed reflected in their classroom practices. Students’ proficiency level, attitudes toward the language, needs, learning styles, classroom environment, and teacher development are six factors that influence the transformation of teachers’ beliefs regarding grammar and grammar instruction into practices. These findings will help broaden the discussion on how to improve the quality of grammar teaching, particularly in the Saudi EFL classroom.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".