In-Service EFL Teachers’ Beliefs about Teaching Reading Strategies
Bibliographic record
Abstract
Recent trends in teacher education have focused on exploring teachers’ beliefs. Earlier studies have shown the important influence of teachers’ beliefs on teaching practices. The present study was conducted to explore the beliefs of Saudi EFL teachers about the significance of teaching English reading strategies. The study aimed also to find the influence of these beliefs on their classroom teaching practice. The participants of the present quantitative study were twenty seven Saudi EFL teachers, teaching in different schools in Riyadh. The questionnaire results were collected and the data was analyzed by the use of SPSS program. In the analysis process, the mean for each item was calculated as well as the standard deviation. Then, the results of the teachers’ beliefs and the actual practices were correlated to find the relationship between them. The results, based on questionnaire data, showed that in-service EFL teachers clearly placed great emphasis on the significance of teaching reading strategies. Teachers were found to believe that the most important teaching reading strategies are: “To guess the meaning of the ambiguous vocabulary” (Mean 4.62), “To explain vocabulary items” (Mean 4.48), “To scan the text” (Mean 4.48) and “To ask questions to check the comprehension of the text” (Mean 4.44), whereas they believe that “To translate words into Arabic” (Mean 2.14) is the least important one. The findings of the current study concluded that what in-service teachers do believe about the efficient strategies of teaching reading, significantly correlates with what they really do in classrooms. The findings suggested that in order to change classroom’ practices, EFL teachers need to change what they believe about these practices.
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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 | 0.005 |
| 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 | 0.000 |
| 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".