Teaching Beyond the Text: How Gender, Experience, and Grade Level Shape Reading Comprehension Instruction among EFL Teachers in Qatar
Bibliographic record
Abstract
Persistent gaps in reading achievement among students of English as a Foreign Language (EFL) in Qatar have raised concerns about the effectiveness of reading comprehension instruction. This quantitative, descriptive study investigates the ways in which EFL teachers’ reading comprehension instruction in Qatar government schools varies according to each teacher’s gender, length of teaching experience, and grade level. Data were collected from 754 EFL teachers via an online questionnaire distributed through the Ministry of Education and Higher Education. The survey measured teachers’ use of reading comprehension strategies and explicit instruction. Descriptive statistics and multivariate analyses of variance (MANOVA) were used to analyze the data. Findings revealed significant trends and variations across these variables. Female teachers scored higher in both the implementation of reading strategies (M = 50.68) and explicit strategy instruction (M = 24.68) compared to male teachers (M = 48.46; M = 23.8). Conversely, no significant multivariate effect was found for teaching experience or grade level, although novice (0–5 years) and secondary-level teachers consistently reported lower strategy use than their peers. The implications of these findings suggest the need for targeted professional development to enhance strategy use, particularly among male and secondary-level teachers, and tailored programs to support novice teachers’ transition from explicit to flexible instruction. The study contributes to a better understanding of the demographic influences on reading comprehension instruction in EFL contexts and informs efforts to improve literacy outcomes through teacher development.
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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 | 0.003 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".