Les effets d’une posture plurilingue de l’écriture collaborative sur l’expérience émotionnelle d’apprenantes créolophones et la qualité de leurs productions écrites
Bibliographic record
Abstract
The purpose of this research was to determine, first, how a plurilingual or monolingual posture adopted during a collaborative writing task influences the emotional experience of Creole learners of French as a second language (FL2), and second, how this emotional experience interacts with the quality of the written production. To this end, 39 FL2 Creole-speaking learners collaboratively wrote texts under two experimental conditions: one imposing the exclusive use of FL2 during the collaborative activity and the other allowing free choice as to the languages to be used. After each task, participants individually answered a self-evaluation questionnaire to measure their emotional state while doing the task. In order to establish a relationship between the emotions experienced by the learners and their writing performance, the texts from both conditions were evaluated using an analytical rubric. The results showed that the participants experienced more positive emotions when they were free to use all their linguistic resources, including their native language (L1). Thus, their emotional experience was significantly more positive in the condition without linguistic constraints. While having access to L1 use contributed to a more positive learning climate, obligatory second language (L2) use was primarily associated with tension and anxiety. Also, participants who experienced positive emotions, regardless of the task, wrote better texts and scored highest on overall quality.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".