Iterative Development of a Digital Game-Based Learning Concept: Introduction of Veterinary Herd Health Management in a Virtual Pig Herd
Bibliographic record
Abstract
A growing interest in the use of digital game-based learning has been identified in veterinary education. Projects in the development of veterinary game-based environments and scenarios are mostly initiated by veterinary institutions, faculties, or instructors; however, the process of development is complex and often involves expertise from a variety of disciplines. In the collaboration between professionals, discussions often arise about content, and how specific elements should be implemented or edited. As discussions are based on the individual experts’ varied disciplines, it can be difficult to achieve a common language, and this leads to blockage and frustration in the development process. In 2012, the University of Copenhagen launched a project on digital game-based learning aimed at veterinary and agriculture students. The overall goal was to develop learning games for herd health management in pig production. The project was carried out in a collaboration between professional game developers, educational/didactic experts, and veterinarians. From early in the process, we identified a need to communicate across disciplines. Therefore, the framework of the Serious Game Development Triangle (SDT) was developed as a tool to facilitate a common language for solving complex issues. The SDT consists of three orientations: games, school, and professionalism. These three orientations are topics that are required considerations when developing a serious game that seeks to teach skills for a specific profession. The SDT contributed to improved understanding across disciplines and made the development process more progressive.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| 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".