Metacognitive Factors Affecting English as a Foreign Language (EFL) Student-writers’ Academic Writing Performance
Bibliographic record
Abstract
Academic writing has a significant role in university education and shapes students’ academic ability. For English as a foreign language (EFL) students, writing is a communicative, goal-oriented, laborious process with emotive, behavioral, metacognitive, and cognitive components. Previous studies have provided insight into specific aspects of metacognition in writing, but there is a lack of synthesized research integrating these components to understand how they collectively influence the academic writing performance of EFL student writers. To fill this gap, this study investigated how metacognition theory contributes to the academic writing performance of EFL students from the perspectives of metacognitive knowledge, experience, and strategies. A total of 370 Chinese EFL student writers were invited to complete one academic writing test and three questionnaires in a classroom setting. The resulting data were analyzed using PLS-SEM. Metacognitive knowledge, experiences, and strategies were found to be positively related to academic writing performance. The results also revealed that, among 10 parameters of metacognition, academic writing performance was more strongly correlated with procedural knowledge, metacognitive feeling, metacognitive estimate, online metacognitive strategies, monitoring, and evaluating parameters. These findings underscore the importance of developing students’ conditional knowledge and promoting effective metacognitive strategies, particularly in evaluating and monitoring, to improve academic writing skills among EFL students.
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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 | 0.009 |
| 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 | 0.001 | 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".