Teaching agroecology through play: a board game on companion planting using agroecological principles
Bibliographic record
Abstract
Board games themed around environmental topics are promising tools for promoting awareness of complex sustainability issues, as the materiality, sociality, and multidimensionality of gameplay can help overcome some of the inherent limitations of more traditional learning formats. To explore the potential relevance of board games for communicating about agrifood system topics in particular, we designed and tested a game called Companion: An Agroecological Adventure, in which players take on the role of community gardeners who must apply both social and ecological principles of agroecology to cultivate a thriving garden plot. In this paper, we present the results of a study in which a group of 50 undergraduate students in an introductory agroecology course at Syracuse University learned, played, and provided qualitative feedback on the game. Our analysis of student comments demonstrates that by immersing players in a simulated environment and providing them with the agency to make and reflect on decisions, playing Companion effectively stimulated student learning about the characteristics of small-scale sustainable agroecosystems. At the same time, the process of playing the game also promoted the development of a variety of relevant skills and competencies, especially students’ ability to think critically, contextually, and holistically about local agrifood systems and their place within them. These results demonstrate that by bringing the spirit of experiential education into the classroom, playing agroecology games can facilitate the type of transformative learning that is critical for promoting meaningful food system reform.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".