La modélisation cognitive, un outil de conception des processus et des méthodes d’un campus virtuel
Bibliographic record
Abstract
Plusieurs projets de modélisation des connaissances sont présentés pour illustrer la variété d’applications possibles de la modélisation graphique par objets typés. Ces applications ont été réalisées dans des contextes et avec des buts souvent très différents. La première application décrit un modèle de la méthode d’ingénierie d’un système d’apprentissage (MISA 4) visant à soutenir le travail de conception pédagogique. La deuxième application comprend trois modèles qui ont été construits pour mieux comprendre certains processus d’apprentissage virtuel et définir des outils de soutien au téléapprentissage. La troisième application décrit les acteurs dans le campus virtuel. En synthétisant ces trois démarches, nous situons le rôle de la modélisation en regard d’un cycle d’acquisition et d’utilisation des connaissances. Several object-oriented modeling projects are presented to illustrate a variety of possible applications of graphic knowledge modeling using object types. The knowledge models described in this article have been designed in various contexts and with various goals. The first application is a model of a method for engineering learning systems (MISA 4.0) that aims to assist instructional designers to use and acquire knowledge of concepts, procedures, and principles in the method. The second application shows models that aim to explain some telelearning processes, as well as their computerized support tools. The third application demonstrates the roles of actors in a virtual campus. Generalizing on these three applications, the role of modeling in a cycle of knowledge acquisition and use is discussed.
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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 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".