# Implementation of the Modern Immersive Learning Model CPLM +
## 3 Results
### Evaluation of CPLM with Brainwave Measurements
During the measured EEG responses, we evaluated the average values of attention and meditation in the different given environments and assessed the average value of the measured EEG responses. In Table 7, we see the measures of attention (CPS-A) and meditation (CPS-M) during the computer problem-solving robotics simulation task and the VR environmental problem-solving task (VR-A, VR-M). Students performed the computer problem-solving robot simulation task during measurements with an EEG device. The time of recording the cognitive activity was 150 s. In the pilot group of students who solved computer problems, we measured a mean value of attention of 43.38 and a mean value of meditation of 54.27. The time recording cognitive activity was 2.5 min. In the pilot group of students solving computer problems, we measured an attention mean of 43.38 and a meditation mean of 54.27. Then, we included the VR environment and measured the same parameters in the immersive environment and with the simulated VR task. As we already noted, meditation is a cognitive activity to relax and calm the mind, and consequently, a higher level of relaxation makes a person more active and less stressed. In the pilot group of students using a VR learning environment, we measured a mean score of attention of 51.44 and a mean score of meditation of 53.36. In Figure 7 we see the measurements represented graphically. Based on the measurements, we can see that attention in the VR environment is 5.94% higher than in the computer problem-solving environment; however, the results are not statistically verified at this stage. The mean value of meditation was slightly lower than for computer problem \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**N** & **CPS-A** & **VR-A** & **CPS-M** & **VR-M** \\ \hline
1. & 42.41 & 41.63 & 49.62 & 40.44 \\
2. & 56.33 & 39.07 & 65.03 & 53.41 \\
3. & 42.82 & 52.92 & 47.81 & 73.74 \\
4. & 48.52 & 52.30 & 59.57 & 53.64 \\
5. & 45.74 & 66.17 & 49.06 & 46.65 \\
6. & 46.13 & 59.81 & 57.03 & 45.03 \\
7. & 48.87 & 79.07 & 50.87 & 54.47 \\
8. & 39.66 & 42.40 & 79.31 & 58.70 \\
9. & 40.18 & 57.88 & 40.41 & 45.33 \\
10. & 55.68 & 58.10 & 59.59 & 70.47 \\
11. & 51.13 & 42.35 & 71.20 & 64.09 \\
12. & 57.28 & 39.38 & 51.73 & 52.55 \\
13. & 75.22 & 52.34 & 38.65 & 42.65 \\
14. & 38.53 & 49.11 & 38.97 & 40.46 \\
15. & 37.20 & 39.12 & 55.27 & 58.78 \\ StD & 9.88 & 11.51 & 11.56 & 10.38 \\ Mean & 48.38 & 51.44 & 54.27 & 53.36 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Computer problem solving attention (CPS-A), computer problem solving meditation (CPS-M), attention VR (VR-A) and meditation VR (VR-M)—scalable measurements in millivolts. solving, which can be justified by the fact that it is an immersive environment and students are less relaxed at the beginning of a test. As we have researched, higher levels of meditation can increase students' ability to pay attention so that they can better absorb and process information. When attention and meditation are high, learners are in an optimal state for learning. Based on EEG measurements and analyses of the articles [37; 38; 39; 43; 45; 46; 47; 48; 49; 50; 51], we can claim with greater probability that VR increases student attention so that they are in an optimal state for learning. As we can see in Table 8, we performed a \(t\)-test to examine whether there were significant differences between the measures of attention and meditation means in a computer environment or in a VR environment. Kurtosis coefficient K for the attention measures (CPS-A K = 2.79, VR-A K =0.78) tells us that the distribution of the measures peaked, and the skewness coefficient S (CPS-A S = 1.44, VR-A S = 0.94) tells us that the distribution of the measures was right asymmetric. Coefficient K of the meditation readings (CPS-M K = 0.18 VR-M K = 0.43) shows us that there were peaked readings, and the skewness coefficient S (CPS-M S = 0.61, VR-M S = 0.59) shows us that there was a right-asymmetric distribution of readings.