Apport de la théorie du sentiment d’autoefficacité pour le développement de la compétence technopédagogique des futurs enseignants
Bibliographic record
Abstract
Le texte s’intéresse à la formation technopédagogique des futurs enseignants, et plus précisément, aux pratiques à déployer en formation initiale pour accroître leur compétence professionnelle à intégrer les TIC. Les pratiques proposées prennent appui sur la théorie du sentiment d’autoefficacité d’Albert Bandura, ce sentiment pouvant influencer leur compétence à intégrer les TIC. Appuyé par des travaux empiriques, le texte aborde les sources susceptibles d’influencer l’autoefficacité à intégrer les TIC dans un cadre pédagogique et identifie des pistes d’action à envisager. Enfin, la réflexion amène à repenser les interventions des formateurs tant en formation initiale qu’en formation continue et à remettre en question, sur le plan empirique, les dispositifs de formation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.017 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.012 | 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".