The Effects of Metacognitive Reading Strategy Instruction on Thai EFL Engineering Students: Metacognitive Strategy Use and Students’ Attitudes
Bibliographic record
Abstract
Metacognition is pivotal in reading comprehension. Therefore, Metacognitive Reading Strategy Instruction (MRSI) is crucial for improving reading comprehension, particularly in English as a Foreign Language (EFL) contexts. The study explores the role of MRSI in enhancing reading comprehension, specifically for 145 engineering students, who were divided into the experimental group (n=82) and control group (n=63). The experimental group was instructed in metacognitive reading strategies, whereas the control group was taught textbook-based instruction. Over 13 weeks, two groups of Thai engineering students were given pre and post-tests on reading comprehension and an attitude questionnaire. Both descriptive and inferential statistics were conducted to analyze the data. The analysis revealed that the experimental group that received MRSI outperformed the control group on the reading comprehension test. The findings showed that the experimental and control groups improved their post-test scores compared to pre-test scores. The experimental group increased from a mean score of 35.30% to 50.04%, while the control group improved from 35.58% to 43.28%. The participants also expressed positive attitudes towards planning (84.32%), monitoring (90.24%), and evaluating (82.95%) strategies, showing a strong emphasis on metacognitive engagement in enhancing reading comprehension. In addition, the data from an attitude questionnaire reflected a positive impact of MRSI on students’ attitudes, with high agreement on the effectiveness of planning, monitoring, and evaluating strategies. The study concludes that integrating MRSI into ESP classes can effectively enhance reading comprehension and provide pedagogical recommendations for its implementation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".