VR Let My Creativity Out: Youth Creating with Immersive Learning Technologies
Bibliographic record
Abstract
With a greater demand for ingenuity and innovation in today’s creative economy, educators need to be creative practitioners inasmuch as students must be, learn, and grow creatively. This qualitative study explores the affordances and constraints of youth creating with immersive learning technologies at a Youth VR Research Camp. A class of 28 students in grade 8, ages 13 and 14, were invited to participate as the co-researchers. The data collection methods included artifact analysis, student-led pair interviews, sharing circles, surveys, and observation. The process of research-creation involved MultibrushVR and FrameVR design challenges focussed on pro-social and environmental change. Findings revealed that the immersive experiences provided flexible learning pathways for the youth to take creative risks, exercise autonomy over their learning, and co-design with peers in novel ways. Although a few co-researchers reported discomforts, such as dizziness or nausea, from using the Meta Quest headsets, a significant 94% expressed interest in further learning through VR. Furthermore, 78% concurred that VR facilitated creative self-expression, while 88% acknowledged having the freedom to make decisions about their learning in VR. These findings indicate that immersive education, when effectively designed, can be a viable strategy for teachers striving to nurture creative learning and creative thinking in their classrooms. The data collected in this study serves as a foundation for future research on the role of immersive education in augmenting creativity and facilitating self-directed learning.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Qualitative | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Qualitative | low |
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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, unvalidatedLabeled directly by 2 models reading the full record.
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".