## 3 Results
### Findings  
#### 3.2.1 Demographics  
The sample (Tables 3 and 4) comprised predominantly male participants, with the online group having a slightly higher representation (69.57%) compared to the face-to-face group (60.87%). The gender distribution aligns with typical patterns observed in \begin{table}
\begin{tabular}{l c c} \hline \hline
**Item** & **Factor 1 *** & **Factor 2** \\ \hline Q1. How would you rate the content of the scenario in terms of & 0.85 & - \\ relevance and accuracy? & 0.78 & - \\ Q2. How would you rate the visual quality of the 3D objects in & 0.75 & - \\ the scenario? & 0.8 & - \\ Q3. How would you rate the smoothness and realism of the animations & 0.68 & - \\ the scenario? & 0.85 & - \\ Q4. How would you rate the overall quality of the learning materials in & 0.8 & - \\ the scenario? & 0.8 & - \\ Q5. How would you rate the clarity and readability of the texts in & 0.68 & - \\ the scenario? & 0.85 & - \\ Q6. To what extent did your activities in the 3D virtual environment & 0.85 & - \\ help you understand the presented topics? & 0.8 & - \\ Q7. Do you feel that this tool positively impact-ed your learning by & 0.8 & - \\ helping you develop new transversal skills such as collaboration and & 0.8 & - \\ problem-solving? & 0.8 & - \\ Q8. What is your overall impression of learning in a 3D & 0.9 & - \\ virtual environment? & 0.82 & - \\ Q9. How would you rate your overall immersive learning experience & 0.82 & - \\ in the virtual environment? & 0.72 & 0.8 \\ Q10. To what extent do you believe teacher’s presence is necessary & & - \\ when undertaking learning activities in a virtual environment? & & 0.72 \\ Q11. Would you consider using a similar educational 3D Virtual & & - \\ Environment for future training? & & 0.8 \\ Q12. How likely are you to recommend this learning approach to & & - \\ other students? & & 0.85 \\ \hline \hline \end{tabular}  
* Note: Loadings above 0.4 are considered significant.  
\end{table} 
Table 2: Confirmatory factor analysis. technology-related studies where male participants often outnumber females [32]. The age distribution was similar between the groups, with most participants aged between 18 and 23 years. The majority of participants in both groups fell within the 18-22 age range, with a slightly higher mean age observed in the face-to-face group (M = 20.96, SD = 1.97) compared to the online group (M = 20.65, SD = 2.21). A notable difference, however, emerged in the participants' experience with computer-based games with the participants of the online group considering themselves to be more experienced (advanced = 52.17%, experts = 34.78%) as opposed to their fellow students in the face-to-face group (intermediate = 39.13%, experts = 13.04%). Experience with Virtual Reality (VR) applications also differed between the groups. The face-to-face group had a more balanced distribution of experience levels (intermediate = 43.48%; advanced = 21.74%). On the contrary, the online group had a higher percentage of beginners (52.17%), indicating less overall familiarity. The disparities in experience suggest that each group brings different strengths to the table. The online group's proficiency with computer-based games might make them more agile and effective in the 3 DVLE which, in turn, could influence their engagement and performance in the instructional simulations. Conversely, the face-to-face group's balanced experience with VR applications suggests that they may have a better foundational understanding of VR, making them potentially more adept at leveraging the educational aspects of the simulations. Therefore, when designing and implementing educational VR tools, it would be beneficial to consider these differing strengths, i.e., tailoring the VR simulations to accommodate both the gaming skills that participants may have and their foundational experience with VR. For instance, initial training sessions could