Comparing Students’ Perceptions and Their Writing Performance on Collaborative writing: A Case Study
Bibliographic record
Abstract
One important strand of research in collaborative writing has been the learners’ attitudes and perceptions toward collaborative writing. This study sets out to compare three pairs of students’ perceptions and their actual collaborative writing practices. Multiple sources of data were collected. Students’ semi-interviews were audio recorded, transcribed and analyzed for their perceptions. Their collaborative writing tasks are also recorded and analyzed for quantity, type and resolution of language related episodes (LREs). The study further examined the collaborative texts using both quantitative and qualitative measures for students’ language improvement. Our finding suggest that most participants expressed positive attitudes towards collaborative writing but only half of them were aware of language improvement. An analysis of pairs’ discussion revealed that participants were overly concerned with vocabulary and all LREs were successfully resolved. The quantitative and qualitative analysis of collaborative texts demonstrated that two pairs received considerable language improvements in terms of lexical diversity, syntactic complexity, fluency and text quality. These findings can be used to encourage students to reflect on their own perceptions and practices in collaborative writing tasks.
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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 | 0.010 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".