Practitioners’ Perspectives on the Application of Integration Theory in the Saudi EFL Context
Bibliographic record
Abstract
There is a close connection between reading and writing. Several studies suggest integrating reading in the instruction of teaching writing skills to English as a Foreign Language (EFL) learner. This study seeks to determine the extent Saudi EFL teachers support, apply and understand the theory of integration between reading and writing. To achieve the research objectives, the researcher compiled two lesson plans; one based on the integration theory and the other, based on a traditional model to see which lesson plan teacher-participants chose to teach writing. The data was then collected through questionnaire containing both closed and open-ended questions to determine the implications of the results in relation to the objectives of the research. The major findings of this research project were that, for the ten EFL teachers surveyed, most of the teachers indicated that they usually taught writing as a separate skill apart from reading, and the written responses from the open-ended questions that was analyzed indicated that the teachers taught writing in the traditional way. The results from the ten participants’ responses also suggested that almost none of the participants were familiar with the concept of integrating reading and writing for the purposes of teaching writing. However, most of the respondents did comment that they agree with the idea of integrating reading in the instruction of teaching the writing skills and given a choice of a lesson plan, most of the teachers choose the integrated lesson plan.
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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 | 0.041 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.020 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 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".