Social Simulations Teach Engineering Student to Gain 'Buy-In' for Human Factors
Bibliographic record
Abstract
This presentation describes social skill development of undergraduate industrial engineering students using ‘social simulations’ in which students interact with trained actors in a designed social scenario. We present the example scenario of a young engineer who must gain buy in from industrial personnel to apply human factors (HF) in production system design. This experiential learning activity was designed with the Interpersonal Skills Teaching Centre at Ryerson University, in response to research evidence that training engineers in HF science alone is ineffective if the organisational environment and culture do not ‘buy-in’ to available benefits. The simulation is enacted by 3 actor/simulators in 2 scenes. They represent the plant manager, human resources manager, union representative, maintenance manager, purchasing agent, and production supervisor for a small manufacturing plant. Students take turns being engineers from head office who have been sent to help design a new production system for improved performance and reduced injury using HF principles. Students must address the concerns of each of the plant’s stakeholders to gain buy-in for this new approach for production development. This presentation will present and discuss the methodology and evaluation options for this technique for teaching social skills to engineering students.
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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 | 0.004 |
| 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) | 0.019 | 0.004 |
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".