Co-constructing Simulations with Learners: Roles, Responsibilities, and Impact
Bibliographic record
Abstract
Co-constructed simulations were designed and piloted with senior occupational therapy master’s students in a neurorehabilitation practice module. The instructor served as the guide for the students through all phases of the case creation, simulation development, delivery, and debrief. The instructor facilitation promoted self-regulated learning (SRL) of knowledge and skill development through independent discovery and peer learning. This paper provides an evidence-informed co-construction simulation design with outlined stages, roles, and responsibilities for the instructor and learner. Thematic qualitative analysis of student feedback highlighted enhanced insight and SRL as a result of multiple role preparation, observation and interaction with peers, close interaction with the instructor, and the multi-stage debrief process. Recommended key features and critical interactions for a successful co-constructed design are also identified for the learner, instructor, and simulation. The co-construction simulation process and design elements are suitable for learners in any health-related field of study.
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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 | 0.019 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.001 |
| 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".