Development of Virtual Tours for Understanding the Built Environment of an Educational Building
Bibliographic record
Abstract
Though we spend a significant amount of time in indoor and built environments as general occupants of residential or commercial spaces, we do not necessarily know how the heating, cooling, and ventilation services work in our occupied spaces. As the mechanical systems of buildings become more complex for energy saving and better indoor air quality, it is beneficial for occupants to learn more their built environment so that they can cooperate effectively for the building’s performance. In this context, the purpose of this research is to develop and evaluate how virtual reality (VR) technology can support occupants in understanding their built environment. An educational building on campus was selected for the development as it provides familiar spaces for potential participants in this research. This research was carried out in two stages. In Stage One, we, as researchers in mechanical engineering, explored the workflow for VR development and developed VR tours for four spaces: a classroom, an auditorium, a conference room, and a mechanical room. In Stage Two, we conducted a survey study to examine the VR experience from the perspective of users. In this survey study, we recruited 34 participants from engineering students/graduates, industry participants, and a sustainability group. The participants generally indicated a positive experience with the VR tours, although the quiz scores on the VR content were weak. From our reflection, we consider that positive and effective VR experiences for the education of the built environment require collaboration from three domains: (1) mechanical systems of buildings, (2) VR technology, and (3) pedagogy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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".