Self-Regulated Strategy Development With and Without Peer Interaction: Improving High School Students’ L2 Persuasive Essay Revisions
Bibliographic record
Abstract
High school students for whom English is a second language (L2) often struggle with effective text revision because of limited ability to self-regulate their writing, that is, to manage the subprocesses of writing and to use writing-related knowledge and strategies. To help students in China acquire effective text revision skills for English persuasive essays, self-regulated strategy development (SRSD) revision instruction was applied both with and without peer interaction targeting knowledge of six writing dimensions. An experimental design involving 120 Chinese 11th-grade students in three conditions, that is, SRSD revision instruction with and without peer interaction (two treatment conditions) and conventional instruction without SRSD (control condition), was applied to examine the instructional effects on students’ writing and text revisions. Analyses of covariance revealed a statistically significant increase in text length and improvement in students’ text quality regarding higher-order content-level writing dimensions in both treatment conditions compared to the control condition in a post-intervention test. Notably, the most substantial improvement regarding both text length and text quality was observed in the SRSD plus peer interaction condition. Further, students in both treatment conditions made more text revisions involving longer text segments aimed at improving quality and changing meaning compared to those in the control condition. Among these, the students in the SRSD plus peer interaction condition exhibited the highest frequency of high-quality text revision during the posttest. The findings provide new insights into the effectiveness of SRSD revision instruction and peer interaction in developing L2 high school students’ self-regulation in revising English persuasive essays.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".