An Educational Tool based on Virtual Construction Site Visit Game
Bibliographic record
Abstract
To enhance the engagement of Civil Engineering students and encourage active learning, a virtual construction site investigation game is developed in the present paper. 3D construction site environment is built based on BIM models and relevant objects common on construction sites are created to enhance the realistic. Navigation and Interactions are developed to enable the students to explore the virtual sites freely and get instant feedbacks. Different modules, such as Questions and Tasks, are developed to exam how well the students master the domain-related knowledge. Unity, a cross-platform game engine, is used as the development platform for this research project. The architecture, mechanism and the implementation are described in detail in this paper. A pedagogical methodology for improving the quality of learning is thus developed by transforming traditional instructional delivery techniques into technology-based active learning. Students’ engagement in the learning process is improved by establishing a contextual connection between ordinary textbook materials and technologies that students use in their daily routines. This new approach enables students to interact, and learn abstract topics in engineering design and construction method. The effectiveness of this active learning method is investigated by the feedback from two groups of students using a questionnaire. The potential benefits of the proposed research are: enhanced understanding of complicated structures; better accessibility to more construction site virtually; more convenient and flexible time for learning practices; and safer site visit with this pre-training tool.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".