# Impact of VR Application in an Academic Context
## 3 Results
### Voice Recognition: Simulation  
To measure the system's voice recognition ability for searching and returning proper results in noisy environments, as well as its efficiency and accessibility for students, we conducted a simulation based on 200 voice samples. The details of the simulation are described in Table 1. The results of the simulation showed that the system can provide accurate answers despite disruptive surroundings. The results indicate that there were few discernible differences between the auto-generated and human samples. Furthermore, the results between male and female voices were similar. The accuracy rate was between 92% (male, both auto-generated and human) and 96% (female human). In Figure 15 the results of the simulation are depicted.  
From the total number of simulations, the system was able to retrieve information in circa 94% of the cases which makes it a good tool for voice recognition in the educational process. To see if anything changes in terms of results, the simulations that failed were executed twice. After the second round, the results were as shown in Figure 16. It seems that the ability of the system to identify patterns and to return results improved by circa 4% in all cases. During the analysis of emotion simulations, the results displayed in Figure 17  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**No.Crt.** & **Question** & **Type** \\ \hline
1 & What is a Petri net? & Single choice \\
2 & What is a graph? & Single choice \\
3 & What is a workflow management system? & Single choice \\
4 & Which from the workflows below are well defined? & Multiple choice \\
5 & What is missing from the Petri net from the figure? & Multiple choice \\
6 & Which from the workflows below are well defined? & Multiple choice \\ \hline \hline \end{tabular}
\end{table}
Table 5: List of quiz questions proposed for quantifying students’ knowledge acquisition.  
show that there are not so many differences between the emotion expressed and the ability to return results. This means that the noise is similarly interpreted by the machine when trying to extract the uttered phrase message transmitted to the system.  
Finally, the results collected for samples with various sound frequencies (low, medium, high) revealed the fact that whispering in a noise environment may cause system difficulties in recognizing voice patterns. The system was able to understand uttered phrases and return results in 70% of the low frequency cases unlike samples with medium and high sound frequencies where the percentage of success was over 90% (see Figure 18).  
In conclusion, the results of the simulation revealed that EduAssistant is well-suited for use in a real classroom as a foundation for the Workflow Management course. The tool may assist students in studying by providing valuable input about the Workflow Management course using voice recognition. The results also showed that voice recognition has an impact on teaching quality in VR mode by enabling more natural and efficient communication between the user and the VR environment. Users can interact with the VR environment using their natural voice, which eliminates the need for typing or navigating menus with a controller. This can lead to a more immersive and engaging experience that enhances the learning process. Voice recognition can provide personalized feedback and instruction based on the user's speech patterns, helping to address individual learning needs. Overall, using voice recognition technology in VR teaching has the potential to  
Figure 16: Second round of simulation for failed cases.  
Figure 17: Simulation results of voice samples based on different emotions.  
Figure 15: Voice sample simulation results.  
improve the quality of teaching by providing a more interactive and personalized learning experience.