EFL College Students’ Writing Self-efficacy and Strategy Use in Their Summary Writing
Bibliographic record
Abstract
Summary writing, illustrating a writer’s level of comprehension and explanation of a text, is an essential but demanding skill in academic writing. However, studies on how individual factors may affect one’s, especially second language (L2) writers’ summary writing abilities remain scarce. One particular focus on effectively assisting learners’ summary writing is to look at the roles of writing self-efficacy and strategy use in summary writing. Therefore, the current study aims to investigate the relationship between writing self-efficacy and strategy use in summary writing tasks and the predictive effects of the two constructs on summary writing performance. Two hundred seventy-two participants were recruited from an undergraduate EAP (English for academic purposes) course in a Chinese university, and they were asked to complete two questionnaires, the Questionnaire of English Writing Self-Efficacy (QEWSE) and the Summary Writing Strategy Use Inventory (SWSUI), before and after a summary writing task, respectively. The correlation results suggested a significant positive correlation between writing self-efficacy and summary writing strategy use. The results of the regression models indicated that the two constructs exerted significant predictive effects on writing scores individually and collectively. Among the subcategories of writing self-efficacy and summary writing strategy, self-efficacy for organization and cognitive strategies were significant predictors of summary writing performance.
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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 | 0.004 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".