Strategies for Better Learning of English Grammar: Chinese vs. Thais
Bibliographic record
Abstract
The success of language learning significantly depends on multiple sets of complex factors; among these are language-learning strategies of which learners in different countries may show different preferences. Needed areas of language learning strategy research include, among others, the strategy of grammar learning and the context-based approach to learning strategies. To fill in these gaps, this study aimed at finding the grammar learning strategies adopted by high school students as well as exploring the national differences between Chinese and Thai students. The results showed that in general the strategies significantly taken up by the high achievers in the grammar test included the metacognitive, the memory, the social and the cognitive. In terms of the national differences, the strategies that characterized the Thai students were the social and the affective. Regarding the Chinese, even though they generally applied all strategy categories at lower frequencies, they were found to prefer different sub-strategies in the following three categories: memory (revision and space reliance), cognitive (note taking) and metacognitive (lesson preview). The findings lead to implications for learners of grammar, interesting future research in grammar strategies and culturally responsive grammar teaching.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".