Bibliographic record
Abstract
The study examined an ESL writing class, which consisted of 36 students, at a community college of Hong Kong. The students took part in three online collaborative writing tasks by sending drafts to peers who gave them suggestions and comments for improvement and working together on the completion of the writing tasks via email. The 36 students worked in small groups of four to six. They wrote, responded and revised using the email system offered by the WebCT interface of their course book. The results were evaluated by means of questionnaire, interview with participating students, report of the peer observer, written work, e-responses and reflective summaries of students.The overall results suggest that students generally enjoyed the supportive atmosphere created by online collaborative tasks and regarded the use of online collaboration as a means of improving their writing by enhancing their motivation, awareness of the audience and the importance of revising, and by reducing their stress and cultivating their positive attitudes towards writing. The data show that the inclusion of the online component has potential in supporting learning and bringing about positive learning effects on writing as learners were found to enjoy the benefit of interacting with the other learners both in-class and out-of-class in the study.
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 | 0.003 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".