Fostering Metacognitive Reading Strategies in Thai EFL Classrooms: A Focus on Proficiency
Bibliographic record
Abstract
EFL readers tend to experience a number of challenges while learning, due to a number of factors that affect how these readers achieve their learning goals. Metacognitive strategies, referring to one’s deliberate, goal-directed control over cognitive enterprises, are considered crucial for assisting EFL learners to be able to accomplish comprehension while reading. Previous studies have enriched the knowledge of metacognitive reading strategies in EFL settings. However, only few investigations yielded statistically significant effects on learners’ reading performance. This present study hence foresees an opportunity to shed new light on this issue by focusing on EFL learners’ proficiency. The objectives of this research are twofold: exploring the effects of the metacognitive strategy instruction on the strategy awareness, and perceiving the effects of the instruction on the reading performance in taking a standardized test. Forty-three students enrolling in a private male school in Bangkok, Thailand participated in the study, lasting ten weeks. A wide range of research tools were administered: SORS, IELTS reading test, and lesson plans. The findings suggested that the students’ awareness of reading strategies used in terms of sub-categories and IELTS reading test score improved with statistical significance. Pedagogical implications and suggestions for future research studies are discussed based on the findings.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".