Game modding: A design cognitive perspective in entrepreneurship education
Bibliographic record
Abstract
This design-based research investigates how game modding—intentional alterations to a game’s original content—can foster a cognitive approach to design thinking among business students, grounded in constructionist theory. A two-stage game-based activity was created and implemented in a Business Game class at a specialized entrepreneurship college in São Paulo, Brazil. Data collection included video recordings, interviews, and student reports. The analysis focused on a representative group of four students, examining the design cognitive processes employed during their redesign journey. Our analysis reveals that due to the absence of a repertoire of previous design solutions, the students grounded their analogies in their own sociocultural context, reflecting their social norms, and through interdisciplinary thought processes. To address the research question, ‘How can game modding support the development of business students’ cognitive perspective in design?’ we propose that game modding serves as a useful pedagogical tool to foster essential cognitive processes in design thinking, particularly within a business education context. Game modding helps develop business students’ cognitive perspective by creating an experiential learning environment emulating the entrepreneurial journey.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.016 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".