Asymmetric collaboration in virtual reality
Bibliographic record
Abstract
It has been established that Virtual Reality (VR) possesses certain qualities for educational purposes. These include the ability to place the learner at the location or in the perspective that the desired knowledge exists (e.g., travelling to another planet or shrinking to miniature size to observe internal anatomy). VR is also considered to contribute with enriching the curricular content, promoting active forms of learning, performance assessment of high validity, and provide the opportunity to teach applied academic knowledge in life-like situations. In regards to teaching mathematics and geometry, three key affordances have been identified; interactive manipulation and construction of three-dimensional geometry, comprehension of spatial relationships, and rectification of abstract problems. Safety protocols and practical guidelines from classroom experimentation have also been formulated by various research projects. In this manuscript, asymmetrical immersive VR in education will be reviewed, as it is relevant for the narrative of learning situations where multiple students use the technology together. As an example, in mathematics, asymmetric VR could be used in contexts where unknown variables must be found in collaboration. The purpose of the narrative literature review is to gain a greater understanding of how asymmetric game mechanics has influence on communication and collaboration between learners. To map the dynamics of this type of learning activity, a taxonomy will be presented. Since VR is still under development in terms of hardware and software, it is important that the current and future technical possibilities are described in a conceptual manner, as well as conclude on optimal coupling between communication dynamics and collaboration mechanics.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| 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".