# Development and Evaluation of Training Scenarios for the Use of Immersive Assistance Systems
## 3 Methodology
### Explorative Pre-Study for an Optimised Training Scenario  
As preparative work, an optimised AR training scenario was carried out in the research association using an explorative study in a mixed-methods design [41]: 10 subjects were trained personally (detailed) or minimally (insufficient) and subsequently asked to perform a subtask from the corresponding system level (hardware, operation system, and AR prototype app), as described below in Section 3.2. Following the methodology of [PERSON] et al. [31] created using case studies on the use and error evaluation, the participants were asked in a questionnaire to evaluate the quality of the training as well as the respective potential for improvement per system level. Additionally, a qualitative failure mode and effect analysis was carried out with the input of the participants, and corresponding actions were identified. These two types of training were evaluated regarding the system levels both quantitatively (performance) and qualitatively (results of the questionnaires and sources of errors). Combining this evaluation with the practical experience of the research project and the implications of the related work led to the development of an optimised training scenario. In addition, specific design guidelines for AR training were derived; see Appendix A. Critical discourse from related work in terms of approaches and methods for developing the optimised training scenario as a result of our preliminary work includes the following. According to [PERSON] et al. [6], training opportunities should generally be offered by the organisation. [PERSON] et al. [6] and [PERSON] and [PERSON] [22] state that informal strategies and the adaptation of learning content to individual needs are also recommended. Based on previous experience from the research project, it became evident that formally, i.e., didactically, structured learning strategies with a guided practical component are effective for immersive assistance systems. This recommendation was also confirmed via the survey. The specific preference (presentation via e-learning, video, trainer, or guided learning by doing) depended on the system levels. The preference for blended learning and the selection of the method depending on the system level were confirmed [27]. The approach of flipped learning, represented by self-study via annotated video and subsequent guided practice, also proved to be advantageous for immersive assistance systems, depending on the system level [28; 29]. Practical experience from the collaborative industrial research project has shown that the methods of the Circuit Parcours Technique [14] cannot be used in industrial AR applications. Trails with group training or topic islands of any kind failed. The industrial users (aged 40-55) could only be successfully taught in individual sessions. This fact clearly shows that tried and tested learning models from higher education cannot be transferred to industry. It is important that any inhibitors that arise during practical training are recognised and resolved immediately; otherwise, immersive systems are likely to be rejected. For this reason, among others, such as social shame (technostress), the Circuit Parcours Technique is unsuitable for this target group. The general model of the learning curve (see Figure 1a; [30]) has only limited applications for immersive assistance systems. The experience of the industrial research project has shown that the learning curves of immersive assistance systems are not characterised by sequential single-stage challenges. As a consequence, a dedicated approach for immersive assistance systems is presented on the basis of this contribution; see Figure 1b. The novice users face challenges at each system level. At first, they must be familiar with the hardware, i.e., position the HMD correctly on their head, which has a major influence on the success of the application and the quality of service [42] (e.g., visibility, hand interaction, consistency of the virtual assets, etc.).