Exploring the benefits of collaborative prewriting in a Thai EFL context
Bibliographic record
Abstract
Although second language (L2) collaborative writing research has demonstrated that texts composed collaboratively are more accurate than individually-written texts, few studies have explored whether collaborative prewriting yields similar benefits. This study investigated whether collaborative prewriting, i.e. interacting with peers during the prewriting phase followed by individual writing, led to higher accuracy, complexity, or analytic ratings than individual prewriting. It also explored the relationship between these text features and student talk during collaborative prewriting. English L2 university students in Thailand ( n = 57) were randomly assigned to write a problem and solution paragraph with either collaborative or individual prewriting. Their texts were analysed in terms of accuracy (errors/word) and complexity (coordination and subordination), and were rated using analytic rubrics (content, organization, language). Transcripts of the collaborative prewriting discussions were analysed in terms of the topic of student talk (content, organization, language, task management, off-task talk). The results showed that the collaborative prewriting texts were more accurate and received higher ratings than the individual prewriting texts. Furthermore, there was a significant correlation between prewriting time and accuracy. Implications for the use of collaborative prewriting tasks in settings for English as a foreign language (EFL) 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 | 0.006 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".