Exploring the Potential of a Serious Game Framework in Developing Systems-Thinking Skills
Bibliographic record
Abstract
Effective decision-making within veterinary practice demands a comprehensive understanding of interconnected animal, public, and environmental health systems. To foster systems thinking, participatory modeling and serious games are gaining prominence. Serious games combine play, instruction, and problem-based learning to facilitate skill acquisition. This study investigates the potential of a multiplayer serious game framework as a participatory method to cultivate systems thinking skills in a Master of Veterinary Medicine program. The research focuses on the Territory Game, designed to encourage engagement and creativity, assessing its role in fostering systems thinking among veterinary students. Integrated into a master's course, the game immerses students in complex decision-making scenarios, aiding their navigation of real-world intricacies. Qualitative analysis of discussions and responses provides insights. Results indicate that serious game-based learning within a participatory structure enhances participants' grasp of decision-making complexities. The game's simulated environment promotes a broader perspective and consideration of diverse factors in choices. Additionally, the game framework exhibits potential to enhance group participation, autonomy, time management, and inclusivity for reserved individuals. However, the study acknowledges that teaching methods like participatory modeling might not universally fit all contexts and could require instructor support. The framework's effectiveness is influenced by educational constraints, engagement levels, learning styles, and expertise. Nonetheless, the Territory Game framework shows promise in deepening understanding of complex veterinary decisions and fostering critical systems thinking skills essential for effective decision-making. Future research should explore its adaptability, scalability, and long-term impact across diverse educational settings.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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".