Logiciels de construction de cartes de connaissances : des outils pour apprendre
Bibliographic record
Abstract
Ce dossier traite des usages possibles de logiciels de construction de cartes de connaissances à des fins d’apprentissage dans un cadre de formation universitaire ou de formation continue. Ces logiciels permettent à l’étudiant de représenter graphiquement un ensemble de connaissances sous forme d’un réseau de nœuds et d’arcs. Le dossier présente quelques logiciels dédiés à la construction de cartes de différents types, mais surtout une revue de stratégies d’enseignement intégrant ces outils ainsi que quelques conseils pour les planifier. Enfin, il résume les principaux avantages et difficultés de la construction des cartes de connaissances au plan cognitif. \n \nThis article examines the possible uses of concept mapping software in a university or continuing education context. Concept mapping applications allow the student to graphically represent compiled information as a network of nodes and vectors. The article looks at several applications that may be used to make different kinds of maps, but mainly reviews teaching strategies that incorporate these tools as well as providing a number of planning pointers. It also summarizes the main advantages and difficulties at a cognitive level in constructing concept maps.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.010 | 0.016 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.019 | 0.004 |
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".