Exploring Female Saudi EFL Teachers’ Instructional Practices in Using Authentic Texts for Teaching Reading Comprehension
Bibliographic record
Abstract
This research study explored EFL teachers’ instructional practices (e.g., adaptation, selection, elaboration, and simplification) in using authentic texts for teaching reading comprehension. In fact, a considerable amount of research has been devoted to investigate the use of authentic texts in teaching reading comprehension. However, there is an academic and professional need to explore how teachers exploit authentic texts while teaching reading comprehension. The study employed a mixed method research design. Teachers’ instructional practices were explored through a self-reported questionnaire. Also, the study examined teachers’ perspectives on these practices and how their Personal Practical Knowledge (PPK) affects their practices by conducting semi-structured interviews. From two Saudi universities, 50 female EFL teachers responded to the questionnaire, while additional five teachers were interviewed. The questionnaire results revealed that EFL teachers adapt authentic texts for advanced students more than beginners. In contrast, most of the teachers in the interviews highlighted that authentic texts are applicable in teaching beginners with the use of technology. The quantitative and qualitative results showed that teachers select authentic texts that match students’ language level, their cultural background, and course book objectives. It was also found that the aspects of PPK knowledge affected teachers’ instructional practices in using authentic texts. Based on the research findings, several suggestions and recommendations were presented to enhance the effectiveness of authentic texts in the EFL classrooms.
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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 | 0.001 |
| Science and technology studies | 0.002 | 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) | 0.003 | 0.001 |
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".