Evaluating the Effectiveness of Integrating Reading and Writing Pedagogy in EFL Setting and Teachers' Perceptions
Bibliographic record
Abstract
The present research argues that the current segregation between the reading and writing skills courses in EFL classes is a hard obstacle in developing the reading ability and writing skills proficiency in Saudi students at the graduate level. The sample included 64 undergraduate female students of a Saudi University, divided equally into the control group and the experimental group. A pre-test and post-test research design was used to collect the quantitative data. Two-tailed t-tests were applied to verify the results. The analysis of elicited data indicates significant progress in the experimental group's mean scores of the post-test when compared to the pre-test at p<.05. The study finds that integrated-skills teaching pedagogy has a significant impact on students' reading and writing proficiency over a short time. A structured questionnaire was administered on n=28 language teachers to identify teacher's perceptions regarding the integrated interpretation and writing courses. They expressed their dissatisfaction with the current segregated reading and writing courses and the learning outcomes. Most experienced teachers expressed a positive attitude towards the integration of these skills. Based on the findings, the researcher recommends incorporating integrated skills pedagogy in teaching reading and writing skills at the university level to enhance the students' performance.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.009 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".