Bibliographic record
Abstract
Construction detail (CD) knowledge is one of the leading learning components in architectural technology (AT) study. The traditional pedagogical method adopts a series of two-dimensional drawings to explain three-dimensional objects. The interactive and immersive features of virtual reality (VR) technology attract attention from the educational sector. While architectural design education has begun exploring integrating VR tools in the classroom, especially in the early design stage, AT is one of the very few subjects that have experimented with VR. This research, undertaken from within a larger, ongoing project, aimed to explore if VR could assist in teaching AT knowledge, especially CD. The project has two phases: phase 1 created several VR lessons that explained specific AT knowledge, using a VR technology currently available for educational purposes; phase 2 adopted a mixed method approach to investigate learners’ experience with the VR lessons created. This paper focuses on the experience in building up a VR learning environment in phase 1. The initial findings after phase 1 showed that the VR technology adopted in this project was not a perfect tool in creating a VR experience in the CD field but could still offer students degrees of virtual reality learning experience.
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 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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".