XVCollab: An Immersive Analytics Tool for Asymmetric Collaboration across the Virtuality Spectrum
Bibliographic record
Abstract
Research has shown that when a group of people collaborate in decision-making scenarios, they can be more effective than when they work alone. Studies also show that in a data analytics context, using immersive technologies could make users perform better in data understanding, pattern recognition, and finding connections. In this work, we are leveraging previous knowledge in Collaborative Immersive Analytics (CIA) and Cross-virtuality Analytics (XVA) to develop an asymmetric system that enables two groups from different places on the Virtuality-Reality spectrum to simultaneously work on analyzing data. We divide users into two groups: the nonimmersive desktop group and the immersive AR group. These two groups can both author and modify visualizations in their virtuality and share it with the other group when they see fit. For this, we designed a seamless interface for both groups to transform a visualization from non-immersive 2D to immersive AR and vice-versa. We also provide multiple awareness cues in the system that keep either group aware of the other and their actions. We designed these features to boost user performance and ease of use in a collaborative setting and incentivize them to rely on the other group for visualization tasks that are difficult to perform on their end of the virtuality spectrum. Our limited pilot study shows that users find the system engaging, easy to use, and helpful in their data-understanding journey within a collaborative context. Going forward, we plan to conduct more rigorous studies to verify our claims and explore other research questions on this topic.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 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".