Modeling Emotions for Training in Immersive Simulations (METIS): A Cross-Platform Virtual Classroom Study
Bibliographic record
Abstract
Virtual training environments (VTEs) using immersive technology have been able to successfully provide training for technical skills. Combined with recent advances in virtual social agent technologies and in affective computing, VTEs can now also support the training of social skills. Research looking at the effects of different immersive technologies on users' experience (UX) can provide important insights about their impact on user's engagement with the technology, sense presence and co-presence. However, current studies do not address whether emotions displayed by virtual agents provide the same level of UX across different virtual reality (VR) platforms. In this study, we considered a virtual classroom simulator built for desktop computer, and adapted for an immersive VR platform (CAVE). Users interact with virtual animated disruptive students able to display facial expressions, to help them practice their classroom behavior management skills. We assessed effects of the VR platforms and of the display of facial expressions on presence, co-presence, engagement, and believability. Results indicate that users were engaged, found the virtual students believable and felt presence and co-presence for both VR platforms. We also observed an interaction effects of facial expressions and VR platforms for co-presence (p = .018 <; .05).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".