Enhancing Learning and Teaching for Architectural Engineering Students uing Virtual Building Design and Construction
Bibliographic record
Abstract
It is important for students in the built environment related disciplines to acquire subject-based knowledge and skills from reflecting on their experience. In return, the learning-by-doing approach has been widely adopted in the academic cluster of built environment. To further strengthen this, this paper proposes a Virtual Reality (VR) based learning and teaching tool. It creates a virtual 3D environment that helps architectural engineering students conceive their design ideas, plan the layout, design the structure, construct the products (buildings, communities, infrastructures, etc.), and directly interact with the products they designed. The objectives of this research are: (1) to build a VR design environment for students to experience corresponding impact from different scenarios, which will help the student understand and investigate different design theories and schemes; (2) to build a VR construction environment for students to investigate how the building is built and what safety issues should be noted when visiting a construction site; and (3) to provide a collaborative environment for students in the Built Environment domain for better communication through a complete building project featuring active and experiential learning. Unity is used to develop the package and VIVE, a VR package, is used to facilitate the immersive interaction between the virtual environment and the users. Students from the Built Environment cluster were invited to use the tool and give feedback using a questionnaire. Positive comments were given by the students showing that they were very interested in studying academic subjects through such a technical game. All of them wanted to play more rounds to improve their performance and to find answers to the questions they failed to answer correctly. Most of the students were willing to spend more time in finding answers after playing that game.
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 | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 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".