Training transfer validity of virtual reality simulator assessment
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract This study utilises computer-based simulations to explore the transfer effects of competency training in maritime education, addressing the current lack of research on their transferability to real-world scenarios. The research explores the accuracy of procedural knowledge assessment using virtual reality (VR), positing that head-mounted display (HMD) VR offers stronger concurrent validity through training transfer measures than 3D desktop VR. This is evaluated by regression on a training transfer condition. It also investigates motivation’s influence on training transfer and the regression model of this relationship. Fifteen marine engineering students were divided into two experimental groups using 3D desktop VR and HMD VR systems, with eight experts in the control group. The students had previously received traditional lecture-based instruction and were given practical training using a 2D desktop simulator in the same scenario as in the VR treatment and in the training transfer condition. The ANCOVA design experiment involved two levels of technical immersion before the operation of real-life equipment. Neither technical immersion nor expertise level as independent variables were found to have a significant effect in the relationship of the assessment predicting the training transfer. The direct relationship was significant ( R 2 adj = 0.436) and further analysed with the influence of motivation, resulting in a moderation model with a decent effect size ( R 2 = 0.740). Based on these findings, we can infer that both types of VR simulations used for assessment demonstrate concurrent validity in predicting real-life performance before we discuss and define the characteristics of the observed transfer according to theory.
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.
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 | 0.004 | 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) | 0.005 | 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 it