[The use of game engine learning as an education strategy in ecohealth].
Bibliographic record
Abstract
This paper analyzes an educational intervention using Game Engine Learning (GELearning) in the project Leadership in Ecohealth for Vector Born Diseases in Latin America and the Caribbean, financed by the IDRC-Canada, and whose training component is coordinated by the National Institute of Public Health of Mexico. GELearning is an educational tool that uses virtual educational games, where participants face real-life situations with clear pedagogical purposes. To learn through GELearning is to simulate situations, very similar to the ones faced in real life. The purpose for using GELearning was to evaluate it as an educational tool, to know the learning impact in participants, as well as to measure how GELearning favored the acquisition of competencies. The results indicate that this tool, besides the benefits already known from the information and communications technologies, contributes to significant learning in an environment that is attractive and stimulating for participants and favors the acquisition of competencies, especially those linked to superior taxonomic levels, which are associated to knowing "how to do" and "how to be".
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".