Development of an Immersive Virtual Reality Prototype to Explore the Social Mechanisms of the Last Planner® System
Bibliographic record
Abstract
A successful implementation of the Last Planner® System (LPS) requires not only education on its principles, but also managing social mechanisms it brings up to reach outstanding outcomes.Simulation games have been widely applied to teach LPS principles, but they do not seem to appropriately capture the social mechanisms due to lack of socio-technical realism and inadequate gaming controls (i.e., control external factors other than one of interest).Immersive Virtual Reality (IVR) technology has the potential to reveal the LPS's social mechanisms by providing a highly-controlled and realistic simulation environment.However, how to effectively leverage IVR for LPS simulation is not well understood.In order to bridge this gap, we identified the essential elements that an IVR simulation should have to study the LPS social mechanisms.We then developed and tested a multi-user IVR prototype with the identified elements to simulate the LPS use in a "hypothetical" construction scenario.The results show that the prototype is feasible for studying LPS's social mechanisms.This study lays a foundation for future research in using IVR simulation games to study LPS social mechanisms.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".