An Interface Design Method Based on Situation Awareness and Immersive Analytics for Augmented and Mixed Reality Decision Support Systems in Construction
Bibliographic record
Abstract
Research on augmented reality (AR) and mixed reality (MR) demonstrated their potential to support decision-making in construction. However, most efforts emphasized technological advancements, often overlooking how to present and interact with information to effectively support decision-making in AR/MR environments. This study proposes an interface design method that integrates situation awareness (SA) and immersive analytics (IA), two complementary frameworks that address user information needs and immersive interaction design. The method guides the design of AR/MR interfaces by aligning information content, presentation, and interaction with SA requirements and IA design principles. To evaluate its effectiveness, the method was applied to develop AR and MR interface prototypes for a simulated decision-making task involving field managers during indoor construction activities of high-rise construction projects. Results show high levels of SA achieved by participants, with no statistically significant differences between AR and MR interfaces, demonstrating the method’s effectiveness to support SA in both environments. The proposed method provides a structured approach for designing immersive interfaces that enable better perception, comprehension, and projection in dynamic construction scenarios. Moreover, it provides designers with practical guidance for interface development and allows practitioners to assess existing AR/MR solutions based on their capacity to enhance SA through IA.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".