Secondary Students’ Perceptions of Their Engagement in a Correcting Process
Bibliographic record
Abstract
This study draws on mediated learning experience (MLE) theory to contextualize the correcting process within the sociocultural dimension of the teacher’s intervention and collaborative learning to facilitate student engagement with discovering, correcting, and rewriting practices. This correcting process was administered to eight mixed-ability groups of Vietnamese secondary students (n = 31) to investigate students’ perceptions of their engagement in the process from both quantitative and qualitative perspectives that have been under-researched so far. The statistical analysis of a closed-ended questionnaire shows that students strongly agreed with the practices and effectiveness of the process, accuracy improvement, approach preferences, and learning motivation. Eight students’ responses to semi-structured interviews elaborated on the benefits and disadvantages of group-correction and the significance of targeting errors, and on each correcting phase. While students’ responses satisfied MLE’s criteria, their perceptions of the limitations of group-correction somewhat qualified the way reciprocity occurred. The findings suggest offering students opportunities to act on language issues in their writing and confirm the usefulness of engagement with correction-feedback practices from which implications for L2 writing and further research are discussed.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".