Comparing Student-Based Context Priming in Immersive and Desktop Virtual Reality Environments to Increase Academic Performance
Bibliographic record
Abstract
Research suggests that 3D virtual environments can be designed to prime engagement, creativity, and improve performance on many cognitive tasks. In this paper, we report on a study that compares the efficacy of context (environmental setting) on the priming of these desired effects within Desktop Virtual Reality (DVR) environments compared to Immersive VR (IVR), viewed from within a VR Head Mounted Device (HMD). We presented a 27-minute seminar “The Creative Process of Making an Animated Movie” to 68 participants within 4 different learning spaces: two with IVR (Prime and No Prime) and two with DVR (Prime and No Prime). The priming scenarios for both IVR and DVR environments included subject matter and popular culture visual artifacts related to animated movies and characters placed within a theatre classroom. This was intended to create a situated learning effect. The No Prime condition was presented in a standard classroom theatre without visual artifacts or any subject matter augmentation. A 20-question multiple-choice content test and UX survey were administered following the seminar while an affective questionnaire measuring anxiety and positive affect were provided before and after the seminar. Increased academic performance was observed with a significant difference in both DVR and IVR priming scenarios compared to the no priming conditions.
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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 | 0.001 | 0.006 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".