New Efficacies for Audience/Performer Interactivity and Responsive Narrative in Immersive Theatre
Bibliographic record
Abstract
Immersive virtual reality (IVR) has substantial possibilities for students to learn remotely through exploration, collaboration, and socialization.IVR is well suited to theatre, which relies on interpersonal attributes to demonstrate actor relationships and character development.IVR theatre can break down the fourth wall to connect audience members and actors in the same physical space, thereby removing the barrier of the stage and placing the audience within the storyline.In this study, a high-caliber youth theatre group was tasked with writing, workshopping, directing, rehearsing, and performing a play for a live audience in AltspaceVR.Grounded by current literature on the affordances and limitations of IVR for learning, this research followed six high school students imagining, designing, and delivering an immersive theatrical performance.Findings report how youth are experimenting with immersive technologies to take traditional theatre in new directions, including audience interaction, responsive narrative, and actors/actresses performing as digital avatars.We highlight pedagogical strategies and design recommendations for working with youth to integrate IVR theatre experiences in secondary education.
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 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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 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".