The Effect of Using KWL (Know-Want-Learned) Strategy on Reading Comprehension Of 5th Grade EFL Students in Kuwait
Bibliographic record
Abstract
Metacognitive reading strategies play an essential role in improving reading comprehension. This study explores the effects of English metacognitive reading strategies and reading comprehension in Kuwaiti primary school students as foreign language learners; this experimental study tries to find a relationship between students' metacognitive strategies, metacognitive strategies, and students' reading performance. Participants were fifth grade EFL students in Kuwait primary education government public schools. The students' reading comprehension was evaluated. Comprehension tracking strategies were measured using Metacognitive strategies (K-W-L Plus). While the experimental groups (B) received instructions according to (K-W-L Plus) techniques, the control (A) group was trained with the traditional teaching approach based on the Kuwait national curriculum school textbooks. A questionnaire investigating the use of English and perceived English proficiency was also conducted. The results revealed that Perceived proficiency in English was not determined by the early or late pre-school age of second language acquisition. Also, bilingual students with perceived proficiency in English had better meta-cognitive reading skills than low perceived proficiency in English. Comprehension monitoring and (K-W-L) strategy was adequate and the most important predictor of reading comprehension among all students in the research sample.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".