Calculating the Productivity and Efficiency of an Educational Product: Transposition of Mario Godard’s Method
Bibliographic record
Abstract
The Office of the educational material’s approval is responsible for approving educational material. The approval process does not include use of the material by students. The productivity of educational products is unknown. The goal of this article is to find a way to calculate the productivity and efficiency of an educational product, to improve it in order to provide a truly effective tool. To do this, we were inspired by the book written by Professor Mario Godard (2010). We will propose ways to measure the productivity and efficiency of an educational product. Finally, we will discuss the difficulties caused by this calculation. RÉSUMÉ. Le Bureau d’approbation du matériel didactique a pour tâche d’approuver le matériel didactique. Cette évaluation ne comprend pas une utilisation par les élèves. La productivité et l’efficience du processus de conception d’un produit pédagogique et l’impact de l’utilisation de ce produit sur la productivité du processus d’utilisation sont inconnus. Ce texte a pour objectif de trouver une façon de calculer la productivité et l’efficience du processus de conception d’un produit et l’impact de l’utilisation d’un produit pédagogique sur la productivité du processus de formation. Pour ce faire, nous nous sommes inspirés du livre du professeur Mario Godard (2010).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.018 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".