A Descriptive Analysis of the Metacognitive Reading Strategies Employed by EFL College Students in Kuwait
Bibliographic record
Abstract
The study measured the awareness and use of metacognitive reading strategies among English as a foreign language (EFL) students at a medical college in Kuwait. The college offers a four-year Bachelor of Science in Nursing (BSN) and a two-year Associate Degree in Nursing (ADN). Data collection involved distributing the Metacognitive Awareness of Reading Strategy Inventory (MARSI) online through Google Forms to a sample of 80 students (Mokhtari & Reichard, 2002). Data were analyzed for strategy use, variations in strategy use between the BSN and ADN students, and the most and the least frequently-used strategies by the participating students. Microsoft Excel software generated the means, percentages, rankings, and standard deviations of strategy use. Findings indicated that the participating students were overall highly aware of metacognitive reading strategies. Moreover, the results showed that while the participating students were high users of problem-solving and global strategies, they were medium users of support strategies. The results also indicated that years of studying English showed a possible impact on the variations in strategy use between the participating students at the BSN and ADN programs. Finally, the analysis revealed that while the most frequently-used strategies among the participants were problem-solving strategies followed by global strategies, the least frequently-used strategies were support strategies. Implications for pedagogy included the need for English teachers to first identify their students’ awareness of metacognitive reading strategies. Second, English teachers can implement evidence-based instruction to maximize the use of students’ metacognitive reading strategies.
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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 | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".