Exploring the impact of digital scaffolding on collaborative writing practices
Bibliographic record
Abstract
This study investigates the impact of modern technological tools, specifically the Framapad online word processor and the Moodle platform, on collaborative writing among second language (L2) students in Algerian universities. The study involved 24 third-year French as a second language students, divided into a control (GT) and experimental (GE) group, each comprising three subgroups. The data were analysed in two phases: initial and revision. Initially, the produced fables were evaluated based on four criteria: structure, content, language, and collaborative writing. In the revision stage, the produced fables were analysed, focusing on the textual changes made—additions, deletions, replacements, and text rearrangements. A comparative assessment was conducted to examine the impact of technology on the quality and creativity of collaborative writing. The results reveal that the experimental group outperformed the control group in structuring fables, exhibiting better integration of plot and moral elements. Both groups addressed Algerian issues, but the experimental group displayed greater creativity. The experimental group demonstrated more effective teamwork and tended to enhance storytelling during revisions by adding more elements and deleting fewer than the control group. These results highlight the potential benefits of integrating technology into language learning contexts to improve writing proficiency and foster collaborative skills.
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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 | 0.004 | 0.021 |
| 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 | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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".