Enhancing EFL Writing Self-efficacy through Templates and Teacher Feedback
Bibliographic record
Abstract
This classroom-based action research examined an instructional strategy developed to address challenges in teaching academic writing to English as foreign language learners. To evaluate the strategy’s effectiveness, 28 first-year college English as a foreign language students received 18 weeks of instruction incorporating writing templates and teacher feedback. A pretest and posttest involving the same writing task were administered at the beginning and end of the course to assess the students’ initial and final writing proficiency. At the conclusion of the course, the students also completed a questionnaire evaluating the influence of the proposed instructional strategy on their writing performance and writing-related self-efficacy. Differences in test performance were analyzed using inferential statistics, and they reached significance. Questionnaire responses were processed using descriptive statistical procedures, with the results indicating that the students held positive attitudes toward the instructional strategy. The findings demonstrated both pedagogical and affective benefits: the strategy enhanced academic writing proficiency and strengthened students’ confidence and self-efficacy in writing. This study concludes by providing suggestions for future research.
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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 | 0.003 | 0.013 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".