Hybrid User Interfaces for Multiple Views: why designer intuition is not enough
Bibliographic record
Abstract
How to arrange multiple views (MVs) in immersive environments to present information is a common problem addressed by human-computer interaction (HCI) and user experience (UX) studies. Specifically, for hybrid user interfaces (UIs) that use multiple interactive devices in an immersive environment, the MV layouts are often based on expert opinion instead of empirically validated guidelines. Thus this paper makes the case that design guidelines for multi-view layouts in hybrid user interfaces are needed as existing guidelines for MV layouts in Virtual Reality (VR), Augmented Reality (AR) and Mixed Reality (MX) environments often are focused on a single interactive device instead of hybrid user interfaces. Moreover, often current guidelines tend to lack empirical support and are not cohesive. Our work further argues that such guidelines need to be based on empirical evidence. In addition, it summarizes existing related work on MVs for hybrid user interfaces and discusses the basics for running controlled experiments and establishing evidence-based design guidelines for the layout of MVs in hybrid UIs.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".