Student Engagement with Teacher Written Corrective Feedback among Chinese Private College Students of Varying Language Proficiencies
Bibliographic record
Abstract
While the efficacy of teacher written corrective feedback (WCF) has been extensively explored, a research gap exists in examining the disparities in how low-proficiency (LP) and high-proficiency (HP) students receive such feedback in second language (L2) writing. Through an analysis of five writing tasks distributed over a 16-week course, this research explored the affective, behavioral, and cognitive dimensions of student engagement with WCF. Six Chinese EFL sophomores, three designated as LP and three as HP students, were selected through purposive sampling. Data collection methods included analysis of students’ L2 writing tasks and stimulated recall sessions. The findings illustrate varying engagement patterns, highlighting LP students’ frustrations and HP students’ reflective and constructive interactions with feedback. These patterns are interpreted through the lens of sociocultural, social cognitive, student engagement, and complex dynamic systems theories, offering a multifaceted framework for understanding the influence of WCF on L2 writing proficiency. These findings also contribute to language learning pedagogy by highlighting the importance of tailored feedback strategies that address the comprehensive dimensions of student engagement to enhance the pedagogical effectiveness of WCF in fostering language proficiency and engagement in EFL settings.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".