From Brynania to Business: Designing an Evidence-Based Business Education Simulation From an Exploration of a Real-Time Blended Model.
Bibliographic record
Abstract
This study provided an opportunity to look across disciplines and beyond regular roleplaying and standard digital-environment-based business games to explore a long-running and unique blended simulation in a different yet related field. The lessons learned from the “anywhere anytime” blended simulation design of the Peace Building Simulation (PBSim) used with undergraduates in Political Science at McGill University in Montreal, Canada provided guidance for the design of a similar simulation for use in undergraduate business management and leadership courses. The results collected through surveys, focus groups, interviews and field observations conducted in 2015 during an exploration of the weeklong simulation suggest that students were highly engaged and productive with this blended format. Interestingly, the participants anticipated they would be both highly engaged and highly stressed during the experience, and those expectations were realized. A learning community was created during the week with the high level of instructor involvement and modelling positively influencing the outcomes. Some gender differences were also found in expectations and engagement. Nine design elements were developed from the results of the study of the PBSim and a review of relevant research. The elements are proposed as useful for the development of a simulation intended to immerse students in a complex business environment.
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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 | 0.016 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".