Circles: A Framework for Creating Inclusive Virtual Reality Learning Activities in Social Learning Spaces
Bibliographic record
Abstract
This paper describes a practical framework for creating inclusive virtual reality learning activities called Circles. Researchers built Circles to address greater engagement and inclusion within virtual reality learning, explore alternative virtual reality learning foundations, and disseminate the design decisions behind creating a virtual reality framework to enhance rather than replace existing social learning spaces. This paper highlights the framework’s experiential learning opportunities and contributions to enhance collaborative learning and increase individual and social inclusion in virtual reality learning activities. These features include supporting multiple virtual reality platforms, connecting different virtual learning environments, supporting symmetric selection interactions, and a networking system to enable collaborative interactions. For preliminary evaluation of the Circles framework from a creator perspective, we summarize and analyze several post-secondary education use cases of the Circles framework and semi-structured interviews with eight creators. The emergent themes from this exploratory analysis suggest that Circles provides a good foundation for social multi-platform virtual reality for learning but that more research in exploring transformational learning and more accessible creator workflows is necessary.
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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 | 0.013 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.006 | 0.018 |
| Scholarly communication | 0.015 | 0.014 |
| Open science | 0.005 | 0.016 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".