# Development and Evaluation of Training Scenarios for the Use of Immersive Assistance Systems
## 5 Discussion
However, the results can be transferred to the extent that the influence of training opportunities on the determinants, EE (which corresponds to perceived ease of use) and PE (which corresponds to perceived usefulness), can be confirmed by the UTAUT2 model. In line with the implications of [PERSON] and [PERSON] [23] and [PERSON] et al. [24], our results confirm that the appropriate QoT can reduce the stress levels caused by ICT. This preliminary work indicates the need for appropriate training scenarios, but no proof hasbeen provided yet. [PERSON] and [PERSON] [10] investigated the effects of training effectiveness on perceived threat, which showed highly significant effects. In our research, the perceived total stress respective workload (Total_Mean) across various subscales of the NASA TLX increased by 20.32 units from OT to MT and by 16.97 units from OT to DT. Other subscales of the NASA TLX also exhibited significant differences (see Table 5). The Mean MD as the psychological response of stress increased significantly between OT and MT by 17.19 units (\(p\) = 0.045; Co'd = 0.846) and between OT and DT by 19.56 units (\(p\) = 0.017; Co'd = 0.963). The physiological responses also supported the impact of insufficient training. HR_Diff increased significantly from OT to MT by 4.91 Hz (\(p\) = 0.045; Co'd = 1.036) and from OT to DT by 8.11 Hz (\(p\) = 0.017; Co'd = 1.712). SCL_Diff exhibited a significant increase from DT to MT by 1.309 \(\mu\)S (\(p\) = 0.008; Co'd = 1.09). But, the results of the SCL_Diff show a conspicuous difference in detail, as DT compared to MT (\(p\) = 0.008) shows a statistically significant difference, but MT compared to OT does not (\(p\) = 0.077). The analyses show negative values for Mean SCL_Diff at OT, i.e., that the SCL was higher in the baseline (resting) phase than in the action phase. This could be explained by the high susceptibility of the measurement system to interference, including electrode wear, skin care products, disinfection of the hands or incorrect positioning, sequence effects during measurement, etc. It should be emphasised that the teaching methods and models from the educational sector are not directly transferable to the industrial context. According to [PERSON] et al. [31] and [PERSON] et al. [6], age, level of education, technology affinity, intention to learn, and organisational conditions, such as the expectation of productive success, etc., play a pivotal role as differentiating factors. This can also be deduced from the large standard deviation in the mean performance in the OT scenario of 2.09%/min. These factors were also confirmed by our own experience from the industrial research project. In summary, we recommend that training for immersive assistance systems should always be based on the user's specific tasks. There should also be as few barriers as possible and a good trainer for troubleshooting. A mix of formal methods with blended learning approaches, personal guidance, and guided learning-by-doing sessions is recommended. It is important to differentiate between system levels, e.g., for hardware and the operational system. It seems advantageous to present the learning content via structured videos with analogies to familiar knowledge (Windows operations). In general, it has been found that when there is a lot of input, learners tend to forget important learning content and become overwhelmed or confused. These findings contradict the implications of [PERSON] and [PERSON] [22], [PERSON] and [PERSON] [14], and [PERSON] et al. [6]. Figure 1b presents postulated special learning curves for immersive systems with different task difficulties and learning efforts per system level. Handling the hardware and the operational system is relatively simple. The main challenge with the operational system (HL2) is the interaction paradigm. The similarity to Windows means that the user journey is easy to manage, based on previous experience.