# Development and Evaluation of Training Scenarios for the Use of Immersive Assistance Systems
## 4 Results  
### Analysis of Data  
Demographic data: A total of n = 52 participants attended this study, 90% male and 10% female. All participants were enrolled as students at a technical university of applied sciences. The participants were aged between 19 and 39 years, and the average age was 25.8 years. Figure 4 shows the results of the experience level in dealing with the mixed reality technologies regarding the self-assessment of the participants. Duration of Training: Descriptively, the duration of training for MT had a mean of 849 s (SD = 83.3), for DT, a mean of 813 s (SD = 246), and for OT, a mean of 1329 s (SD = 189). QoT: Every condition of the independent variable QoT led to significant changes in all dependent variables, which are presented in detail below and highlighted with a star (\"\"). Performance: According to the performance, a statistically significant difference was shown for QoT F(2, 29.6) = 12.83; \(p<0.001\); \(\eta^{2}\)P = 0.40; and power = 0.99. The conditions of normal distribution and the variance homogeneity were violated. [PERSON]'s ANOVA was calculated as compensation, which is robust for n > 30 with the absence of a normal distribution. The post hoc comparison with the Scheffe alpha correction shows statistically significant differences between MT and OT t(49) = \(-\)5.41; MD = \(-\)2.47%/min; \(p<0.001\); and d = \(-\)1.828, as well as between DT and OT t(49) = \(-\)4.12; MD = \(-\)1.88%/min; \(p<0.001\); and d = \(-\)1.392. Figure 5 shows the corresponding boxplots. OP: For the OP, a statistically significant difference within the independent variable QoT F(2, 49) = 5.28; \(p=0.008\); \(\eta^{2}\)P = 0.177; and power = 0.84 could be proven via ANOVA. Figure 4: Distribution (%) of experience level in hours. Figure 5: Boxplot of the performance (%/min)–quality of training (QoT). Here, the variance homogeneity is met, while the normal distribution is violated. Due to the large sample size, the violation can be compensated for. The post hoc comparison with the [PERSON] alpha correction demonstrates a statistically significant difference between MT and OT t(49) = \(-3.103\); MD = \(-21.46\%\); \(p\) = 0.009; and d = \(-1.049\). Figure 6 represents the corresponding boxplots with the mean, median, and IQR. TOTP: The statistical significance of the difference between QoT could also be proven for TOTP F(2, 28.8) = 21.67; \(p\) < 0.001; \(\mathrm{\SIUnitSymbolMicro n^{2}p}\) = 0.51; and power = 0.99. Since the assumption check of homogeneity was violated, [PERSON]'s ANOVA was applied. Moreover, the distribution of normality was given. The post hoc comparison with the Scheffe alpha correction indicates statistically significant differences between MT and OT t(49) = 6.592; MD = 593.3 s; \(p\) < 0.001; and d = \(-2.230\), as well as between DT and OT t(49) = \(-5.688\); MD = 511.9 s; \(p\) < 0.001; and d = \(-1.924\). Figure 7 shows the corresponding boxplots with the mean, median, and IQR. UTAUT2: The subscales of UTAUT2 were tested for differences between the three levels of the independent variable QoT using ANOVA; see Table 3. The condition of variance homogeneity is met for all subscales. The normality distribution is only violated for the scales SI, FC, and HM. However, an ANOVA is robust against this violation if the sample size is large enough (see above), as in this case with n = 52. The primary focus is on behavioural intention. The results show a statistically significant difference between the training scenarios. Training also influenced the acceptance factors PE, EE, FC, and HM. Figure 6: Boxplots of the OP–QoT. Figure 7: Boxplots of the TOTP (s)–QoT. A post hoc comparison with Tukey alpha corrections was also carried out. For the subscales PE, EE, FC, HM, and BI, statistically significant differences were found between MT and OT. The corresponding statistical parameters can be found in Table 4. Figure 8 shows the corresponding bar chart. NASA_TLX: To assess the perceived overall stress, the Total_Mean was calculated across all subscales of NASA TLX.