A Mixed-Method Analysis of Usability Study of Video and VR Safety Training: Towards Implementation of VR in Working at Height Training
Bibliographic record
Abstract
Falling from height is considered one of the top causes of workplace injuries and fatalities in the construction industry.The regulatory WAH training, conducted in-class and lecture-based, has been successfully implemented; however, its effect is modest.This study aims to find the relationship between the traditional method and VR simulation in terms of user perception.A crossover design was adopted where participants experienced video and VR training in different sequences.Widely used SUS to measure perceived usability and a VR perception questionnaire was implemented.The two-factor analysis of SUS resulted in new usability and learnability.The result shows no significant difference in perceived usability between the training methods.However, on further analysis, one group found video easier to learn.Similarly, there was a significant inclination of users towards VR training in terms of preference, engagement, and ease of remembering.The Spearman correlation revealed older participants perceived the VR interface as less usable.It was also observed that the training order with video first followed by VR perceived the overall system better as compared to the other group.Further suggestions using qualitative data analysis are proposed.
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 | 0.023 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".