Cloud-Based Collaborative Writing to Develop EFL Students’ Writing Quantity and Quality
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This study has devoted itself to examining the effect of using cloud-based collaborative writing on EFL students’ writing quantity and quality. Twenty-one EFL students studying at Jubail College of Education, IAU University participated in the study. They were pretested in writing quantity and quality before the treatment then they practiced cloud-based collaborative writing for a whole semester before being posttested in both measures. Results from non-parametric Wilcoxon Signed Ranks revealed no significant difference between the pretest and the posttest regarding writing quantity whereas it revealed a significant difference with regard to writing quality. It was concluded that cloud-based collaborative writing can be used to improve the quality of essays written by EFL students. Recommendations and research suggestions were introduced.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it