Students’ Experience with a Virtual Reality Tool: Brain Stories
Bibliographic record
Abstract
Virtual Reality (VR) has been applied at the higher education level to teach students about a variety of topics. This paper documents the experiences of higher education students with a VR tool, Brain Stories, as part of quality improvement funded by an IDEAWORKS Catalyst Fund grant. This tool introduced students to fictional characters diagnosed with a brain disorder: Aaron with autism, Henry with schizophrenia and Linda with Alzheimer’s disease. This tool was introduced to build interest in learning while developing empathy through the first-person perspective used with characters. In total, 41 students (2 male; 39 female) provided feedback about their experiences. When asked if they would recommend this tool, 31 students (84%) said “yes”. A qualitative analysis of students’ responses revealed the following themes: Contribution to Learning, Person-Centered Perspective, Immersive Experience, and Suggestions for Improvement. Recommendations are provided for how VR can be incorporated in future postsecondary classrooms in accordance with Universal Design for Learning principles and the development of a Community of Practice.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".