Le Campus virtuel: un réseau d'acteurs et de ressources
Bibliographic record
Abstract
A model of the virtual campus is explored and presented resulting from work started in 1992 at Tele-universite and its research centre, LICEF. The Virtual Campus model is based on the networking of diversified actors and resources. Its aim is to offer an access, in real time and in asynchronous mode, to a variety of learning resources: trainers and tutors, content experts, managers, designers. These different actors have access to computer-based servers offering multimedia documents, courseware, integrated tools for task achievement and training, files of individual or group messages, projects and activities. We present here a survey of six different TeleLearning models, then the LICEF's Virtual Campus model, its actors and their functions, the process into which they participate and the roles they play, as well as the five virtual spaces of resources in which they navigate. We also present five different implementations of the model and outline future research and development in this field. Le concept de campus virtuel presente ici resulte de travaux entreprise depuis 1992 a la Tele-universite et a son centre de recherche, le LICEF. Le Campus virtuel propose repose sur la mise en reseau d'acteurs et de moyens diversifies. Il vise a offrir un acces, en direct et en differe, a diverses ressources d'apprentissage : formateurs et tuteurs, experts de contenu, gestionnaires, professeurs-concepteurs. Ces differents acteurs ont acces a des serveurs informatiques leur offrant des documents multimedias, des logiciels, des outils de travail et de formation, des fichiers de messages individuels ou collectifs, des travaux a realiser. Nous presentons six modeles techno-pedagogiques de formation a distance, puis nous exposons le modele de Campus virtuel du LICEF, ses acteurs et leurs fonctions, les processus auxquels ils participent et les roles qu'ils y jouent, ainsi que les cinq espaces virtuels dans lesquels ils evoluent. Nous faisons aussi etat de cinq implantations du modele, ainsi que des perspectives de recherche et de developpement dans ce domaine.
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
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
| gpt | no category Domain: not available · Genre: Other About the Canadian research system: no · About a Canadian topic: no | Theoretical or conceptual | low |
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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.017 |
| Scholarly communication | 0.015 | 0.017 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".