The Influence of Written Corrective Feedback on Students’ Learning Engagement in Writing: A Longitudinal Comparative Study of Middle and High School EFL Students in China
Bibliographic record
Abstract
In the process of writing, EFL students produce language output, which tests their ability to use language comprehensively. Teacher feedback is an important bridge between learners and teachers, which is the most common and important form of feedback that cannot be replaced. This study aims to explore the effects of teachers’ written corrective feedback on students’ learning engagement in English writing at different ages from the perspective of students’ intrapersonal factors. The data explored in this article are collected by questionnaire from students in a junior and a senior high school both located in Guangzhou. The questionnaire uses the most authoritative method to classify learning engagement into behavioral, cognitive, and affective dimensions. The specific research question is whether are there any differences in learning engagement between the two age groups. The findings indicate that there are significant differences in the three dimensions of learning engagement between the two age groups. Generally, senior high school students’ degree of learning engagement is lower than that of junior high school students. Based on the detailed analysis of the differences in specific learning situations, this paper gives some specific suggestions for English teachers’ writing teaching practice.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".