Quelle place pour les TIC en formation initiale d’enseignants de français ? Le cas de l’Afrique
Bibliographic record
Abstract
Cet article présente les résultats d’une étude menée dans le cadre de l’Initiative francophone pour la formation à distance des maîtres (IFADEM). Ce projet, copiloté par l’Organisation internationale de la Francophonie (OIF) et l’Agence universitaire de la Francophonie (AUF), a pour objectif le développement d’un dispositif hybride qui associe formation traditionnelle, utilisation des technologies de l’information et de la communication (TIC) et méthodes de formation à distance pour l’enseignement du français. La première expérimentation a pris place dans quatre pays, dont trois d’Afrique : le Burundi, le Bénin et Madagascar. Dans le prolongement du projet IFADEM, cette étude a pour but de dresser un portrait de la place des TIC dans la formation initiale des enseignants de français en Afrique. Nous en concluons que l’intégration des TIC dans la formation initiale des enseignants de français est en cours, ce qui nous amène à recommander des pistes pour une utilisation pertinente des TIC.
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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.013 | 0.007 |
| Scholarly communication | 0.012 | 0.006 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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".