Identifying Interactions for Virtual Reality; a Study of Collaboration and Engagement in Different Media
Bibliographic record
Abstract
Designers use many methods and techniques to develop ideas and quickly generate concepts.Often these include collaboration and engagement with post-it notes.Technology has enabled exercises using post-it notes to be accessed beyond the physical realm, as digital forms of the post-it-note exist in software applications.This study focused on the experience of using collaborative tools for design thinking practices using post-it notes in face-to-face and digital activities in order to discover common features that could be applied in the design of virtual reality design thinking tools.It found that some common activities in face-to-face collaborative design thinking interactions among designers, such as navigation, selection, manipulation, text input, among others differed from interactions in 2D and 3D Virtual Environments.Study participants worked on design thinking exercises in three different teams using three different types of media (face-to-face, 2D digital, and 3D virtual reality).The teams' behaviours were analyzed to identify and classify the different kinds of interactions that took place.Once these interactions were identified, common and unique gestures were grouped for further analysis.Patterns of activity intensity were identified within and across teams, providing the opportunity to make recommendations about features that could be improved to better suit design collaboration and the user experience in virtual reality.
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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 | 0.007 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.012 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".