Game design as reflective pedagogy in entrepreneurship education
Bibliographic record
Abstract
The relationship between business education and games, primarily simulation games, dates to the 1930s in Europe and the 1950s in the US and continues to grow today. These games are often used to teach action-oriented disciplines, such as marketing, strategy, and entrepreneurship, providing situations in which decision-making under risky circumstances can be practiced. Our research shifts the focus from playing games to designing them for learning. Our game design activity brings the ambiguity and uncertainty from the entrepreneurial environment into the classroom, where risks can be taken, and failure is part of the experience. Students dive into an iterative design process grounded in inquiries and investigations. This requires communication and negotiation skills, aiming to construct dynamic business representations in constant conversation with the situation. Although students' lack of professional experience and game design knowledge poses challenges, the design activity uncovers many learning possibilities and discoveries. Our pedagogical approach attempts to embrace constructionists’ learning characteristics by using a board game to mediate the design process, besides the gameplay. Students' designs should not only reflect their problem-solving experiences, identities, and understandings, but also construct meaning through conceptualizing business interpretations as game elements, mechanics, and rules. Through observations in university classroom settings (Canada, France, and Brazil), we present examples of students' game designs and how they interpreted entrepreneurial challenges through their designs. By employing design thinking to create conceptual representations in a meaningful game, they showed their understanding of entrepreneurship, worldviews, and contextual knowledge grounded in their sociocultural contexts. A board game became a tool that embodied design possibilities based on low-cost resources and was used in classrooms without computers, internet access, or other technical devices. A board game provides an inclusive and immersive learning experience that supports better conceptual understandings of systems, such as entrepreneurial activity. This research contributes not only to understanding the role of board games in business education (in contrast with prevalent digital simulation games) but also motivates the next generation of entrepreneurial educators to experiment with novel teaching methods.
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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 | 0.011 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.018 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".