# Development of a Virtual Reality Tool for Train Crew Training
## 4 Results
### Analysis of the Processing Times of Training Activities  
In the first stage of the study, the execution time of contracted operations was measured using a virtual training tool. This measurement was to determine the following:  
1. The time intensity of the execution of individual operations;
2. Identification of activities with high variation in execution times, which may indicate a strong dependence of the execution of the operation on the predisposition of the trainee;
3. Identification of activities with low variation in execution times but a high parameter of average execution time--identification of activities that are difficult in virtual reality, regardless of the predisposition of trainees. To carry out the research task defined in this way, the obtained time measurements were subjected to quantitative analysis based on three parameters: arithmetic mean, standard deviation, and coefficient of variation. Table 2 shows the obtained results arranged in descending order according to the coefficient of variation. \begin{table}
\begin{tabular}{c c c} \hline
**Symbol** & **Activities** & **Visualization** \\ \hline A8 & Elevator raising with manual lever \\ \hline A9 & Closing the elevator and exiting the train \\ \hline \end{tabular}
\end{table} 
Table 1: Cont. The analysis of the most time-consuming execution of the indicated handling operations by individual participants in the experiment was aimed at identifying which operations cause the most difficulty in execution and whether the identified difficulties are due to the lack of skill relative to trainees, the incorrect representation of reality in the virtual environment, or the complexity of the operation itself. Therefore, an additional analysis was carried out on the impact of a trainee's experience on the time he or she takes to perform specific tasks. For this purpose, data from a pre-training survey of participants were used, in which they specified, among other things, their experience of regularly playing computer games and virtual reality. As can be observed in Table 3, participants with experience using VR tools obtained significantly shorter execution times per iteration of the training cycle, even if they did not play regularly on computers. In contrast, the times are significantly longer for participants using VR tools without experience. For those who additionally do not use computer games, the average time to complete a full training iteration increased by 7 min. The most time-consuming operations were found to be A2 (03:35), A1 (03:22), and A6 (03:15). Two of them (A1 and A2) also have the highest variance in their performance by trainees. In the case of activity A1, the value of the coefficient of variation was higher than 1, which means that in the group of volunteers, there were people who performed this activity in a relatively short period of time. At the same time, some people could not cope with the activity. This could mean that the activity was mapped incorrectly in the virtual environment and requires a certain correction. Activities that required in-depth qualitative analyses were singled out based on the analysis of the variability index's value. These activities included the following: