Decoding Success: Investigating the Impact of Trait and State Strategies on Reading Test Performance among Thai High School Learners
Bibliographic record
Abstract
The field of second language (L2) acquisition has increasingly emphasized learner strategies, cognitive and metacognitive methods, as crucial factors in individual differences and reading comprehension. Grounded in Bachman and Palmer’s communicative language ability (CLA) model, this study investigates how strategic competence in cognitive and metacognitive strategies impacts reading comprehension among Thai high school students. By employing quantitative methods, including structural equation modeling (SEM), the study explores how trait strategies (perceived strategic knowledge) and state strategies (actual strategy use) influence reading test performance. The research involved 685 students from a public high school who completed Likert scale questionnaires about their strategy use before and after comprehension tests. Results reveal that state strategies are employed more frequently than trait strategies and show a significant positive correlation with reading performance. While cognitive strategies like comprehension and memory are critical for understanding text content, metacognitive strategies like planning and monitoring improve learners’ ability to regulate their strategic application. However, evaluation strategies were found to be less frequently used. The study’s findings advocate for balanced training in both cognitive and metacognitive strategies to bridge the gap between strategic knowledge and effective application, thereby empowering learners to become more autonomous and proficient readers. Further research should investigate strategic competence in different cultural contexts, employ longitudinal studies, and expand the research to other language skills to gain a comprehensive understanding of L2 learners’ strategies.
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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 | 0.001 | 0.001 |
| 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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".