Collective Digital Storytelling: An Activity-theoretical Analysis of Second Language Learning and Teaching | Les histoires numériques collectives : une analyse systémique de l’activité d’apprentissage-enseignement d’une langue seconde
Bibliographic record
Abstract
This paper describes the collective activity of a group of four students who created a digital story as a teaching resource that was to be used for teaching English as a foreign language. It uncovers and analyzes the actual processes underlining the activity as it unfolds from one stage to another. Four processes, viz., sociocognitive interactions, methodological processes, reflective processes and techno-semiopragmatic interactions were identified during the unfolding of the collective activity. Moreover, the results of this study highlight the role of the community in determining the activity of a lower level language group. Cet article décrit l’activité collective d’un groupe restreint de quatre étudiants qui a créé une histoire numérique en tant que ressource pédagogique pour l’enseignement de l’anglais langue étrangère. Il analyse et révèle des processus de l’activité lorsqu’elle se déplie d’une phase à une autre. Quatre processus, à savoir les interactions sociocognitives, les processus méthodologiques, les processus réflexifs et les interactions techno-sémiopragmatiques ont été identifiés. Les résultats de cette étude soulignent également le rôle de la communauté pour déterminer l’activité d’un groupe restreint ayant un niveau linguistique inférieur par rapport aux autres groupes.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".