Interactions plurielles d’étudiants en autoformation guidée et autonomisation | Interactions and Autonomization of Students in a Guided Self-Learning Environment
Bibliographic record
Abstract
Nous interrogeons les formes d’interactions des apprenants dans un parcours d’autoapprentissage guidé en langues. Le dispositif comprend un centre de ressources en langues, des entretiens et un journal de bord réflexif sur leurs activités dont certaines visent des interactions : tandem, réseaux sociaux, etc. À partir de questionnaires et d’extraits de journaux de bord, nous proposons une typologie des interactions qui nous conduit d’une part à interroger les apprentissages formels, non formels ou informels, d’autre part à relier ces interactions aux différentes catégories de l’autonomisation : autodirection, planification et choix des ressources, autorégulation et choix des stratégies, autoévaluation.We analyze the way interactions take place among learners in a self-directed language learning environment. It gathers a language learning centre, individual interviews and a reflexive learning journal describing their activities, which may include interactions such as tandem, social networks, etc. We rely on questionnaires and learning journal extracts that help us build a typology of interactions. This leads us to discuss the notion of formal, informal and non-formal learning and to associate these interactions with several categories for the development of autonomy: self-direction, planning and choosing resources, self-regulation and choice of strategies and self-evaluation.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".