Innovative teaching strategy for promoting academic integrity in simulation
Bibliographic record
Abstract
Maintaining academic integrity is a universal problem and can be especially difficult when implementing a simulation scenario that must take place over several days. It became obvious to faculty that students scheduled in later sessions exceeded realistic expectations in their performances. In response to this, faculty created two scenarios (one psychiatric and one medical-surgical)with flexibility that provided each student a unique and challenging learning experience while guiding the facilitator along various pathways based on the student’s actions in the scenario. This allowed the overall learning objectives to be maintained regardless of students sharing information from simulations scheduled on earlier dates. Adapting the scenario based on individual student’s responses allowed each student to have a unique learning opportunity in spite of the students being “prepped” by students that had already participated in the simulation. Faculty and student feedback revealed the flexibility of the scenarios was a valuable and meaningful learning experience. This paper discusses how to plan and implement this innovative approach to simulation, which will help to counter the effects of information sharing among students.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".