Devis ludique pour les modèles d’ingénierie de dispositifs pédagogiques | Gamification Specifications for Engineering Models of Educational Devices
Bibliographic record
Abstract
Educational devices can include play-based elements, and even take the form of so-called serious video games combining educational and playful aspects. Educational engineering models, however, do not take into consideration the addition of play-based features in educational devices. As for engineering models targeting serious games, they are often designed for a specific genre. In this article, we propose the addition of gamification specifications to the ADDIE model for use in the development of serious games. The example used ultimately highlights the adaptability of the model created, which allows the modification of later versions of a serious game.Les dispositifs pédagogiques peuvent comporter des éléments ludiques et même prendre la forme de jeux vidéo dits sérieux qui combinent aspects pédagogique et ludique. Cependant, les modèles d’ingénierie pédagogique ne prennent pas en considération l’ajout de caractéristiques ludiques aux dispositifs pédagogiques. De leur côté, les modèles d’ingénierie ciblant les jeux sérieux sont souvent conçus pour un genre précis. Dans le présent article, nous proposons d’adjoindre un devis ludique au modèle ADDIE afin de l’utiliser pour l’ingénierie des jeux sérieux. Au final, l’exemple utilisé souligne l’adaptabilité du modèle créé qui permet la modification de versions ultérieures d’un même jeu sérieux.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 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, 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".