Design and development of virtual learning environment for animal experimentation
Bibliographic record
Abstract
This research is part of the VEA (Virtual Environment for Animal experimentation) project, conducted in collaboration with the Biological Engineering Department at the Laval University Institute of Technology, France. The primary objective is to develop a virtual reality platform that reduces the need for animal use in training by providing an immersive learning environment where students can master technical procedures and gestures. In this article, we present a novel VR-oriented pedagogical model that was used to guide the design of a virtual learning situation corresponding to a use case involving the placement of a catheter in an anesthetised rat. The development process incorporated pedagogical considerations and technological implementations, emphasising a user-centred design approach. To evaluate the usability of the VR application, a preliminary face validity study was conducted with 146 participants. The study used questionnaires to collect subjective data on user experience, interaction quality, and overall satisfaction. Results demonstrated high usability scores and positive user feedback, indicating the effectiveness of the VR application as a training tool. Key contributions of this work include the detailed blueprint for constructing VR-based educational situation and the empirical validation of the application’s usability. This research supports the potential of VR to replace traditional animal-based training methods, improving both ethical standards and educational outcomes.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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".