Exploring Metacognitive Strategies Employed by ESL Writers: Uses and Awareness
Bibliographic record
Abstract
The present study explored metacognitive strategies employed by English as second language (ESL) writers. The study also investigated students’ awareness of the effectiveness of these strategies and the relationship between students’ language proficiency levels and the frequent uses of metacognitive strategies. The data was collected via a questionnaire completed by non-native English speaker students (23 males, 38 females) at a midwestern university in the United States. The findings indicated that students frequently employed the three components of metacognitive strategies (i.e., monitoring, planning, evaluating; where evaluating was the most frequent strategy, followed by monitoring and planning). Moreover, the results indicated that students had a relatively high awareness of the effectiveness of the strategies discussed in the study which consequently affected students’ uses of these strategies during a writing task, such as essays. Among the students, there was a positive correlation between students’ proficiency levels and the frequency of the use of strategies. The study suggested several pedagogical implications including the need for increasing students’ as well as teachers’ awareness of metacognitive strategies in teaching and learning academic writing.
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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 | 0.010 |
| 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 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".