UTILIZATION OF VIRTUAL REALITY FOR ENGINEERING DISCIPLINE SELECTION
Bibliographic record
Abstract
Fully immersive Virtual Reality (VR) has the ability to take students to remote locations or allow them to interact with a simulated environment without having to leave campus. This study investigated whether VR is an effective tool to help students increase their knowledge and understanding of various engineering programs and as a result increase their confidence when selecting an engineering discipline. Two purpose-built VR applications were developed; one for Mining Engineering and one for Engineering Chemistry at Queen’s University. The intention of the VR applications was to introduce students to the program of study by bringing them directly into the classroom or lab environment as well industrial sites where engineers in that discipline work. Each VR application took 5-7 minutes to complete and was intended to be interactive. To determine if the VR applications increased the student knowledge, understanding and confidence when selecting an engineering discipline, a series of questions were added to the annual first year engineering survey at Queen’s. Results from the study showed that the VR applications strongly or moderately increased the knowledge and understand of numerous students who attend the Mining Engineering and Engineering Chemistry discipline selection nights. Furthermore there were a number of students whose confidence when making their discipline selection was enhanced by the VR applications.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".