Pedagogical Practices Associated With Sophisticated Pedagogical Scenarios Using VR Simulations in Science Courses
Bibliographic record
Abstract
In the context of the documented decline of student interest in science, ascribed to a high level of concept abstraction, the sheer quantity of science concepts and teacher-centred teaching approaches, we tested the potential of desktop VR (DVR) simulations to engage students.The literature shows that the activities and support built around the simulations themselves are of utmost importance.In this design-based research involving 39 faculty and 5,780 students, the research team and the pedagogical team accompanied teachers in their development of pedagogical scenarios, with tools derived from the NRF/Jeffries (2022) model in nursing simulations.Scenarios were documented through individual teacher interviews.A multilevel regression analysis to predict the students' behavioural engagement showed that the scenario score, associated with high-quality scenarios, is the single and most important level-2 variable associated with the teachers.Pedagogical practices associated with high-scores scenarios were analysed and compared to those associated with low-scores for the prebriefing, briefing, simulation and debriefing phases.Sophisticated scenarios are characterized by more activities in the briefing and debriefing phases, as well as by collaborative activities.
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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 | 0.006 | 0.049 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".