Use-case and learning scenario

In this chapter, a further practical example will be carried out. The department of thermodynamics is finding it difficult to transport their domain-specific knowledge to other departments or customers. This knowledge-related problem can be solved by providing additional information such as learning material and descriptive visualizations. Additionally, they face the challenge of training and teaching new researchers or students the use of thermodynamics test beds. In this chapter, the following questions are addressed:

• • RQ1: Are MR applications suitable for supporting learning scenarios in which students or new employees learn how to use thermodynamic test beds?
• • RQ2: Gan MR applications be used to foster the transfer of domain- specific knowledge to experts of other domains or to customers?

This section describes the life cycle of a thermal test bed, beginning with the Gomputer-Aided Design (GAD) construction, followed by the assembly and start up procedure of such a test bed. The main motivation of using a MR application in a learning scenario is that new employees or students can already train to use the test bed during the design phase of the test bed. Usually, they must wait until the whole test bed is built and operational. The research idea is that during the assembly phase the missing parts can be placed as holograms in the field of view to verify the correct position and to get a better understanding of how the test bed will be operational when it is finished. Additionally, basic training scenarios such as localization of the parts within the test bed can be performed in early stages. Figure 10.2 shows a comparison between the usual learning approach and the MR learning scenario. Usually, in the CAD phase there is no learning material available since the test bed is in the construction phase; the only available data is the CAD, which is improved iteratively. During the assembly phase, some changes can be made to the test bed. In the usual learning approach, the learner can only perform learning scenarios while using the test bed in the operational state. In the MR scenario, the learner should be able to walk through simulated testbed experiments in VR mode. The two modes of the HoloLens app are explained in detail in the Prototype section. For the VR mode, one reasonable state of the CAD is sufficient to create a VR learning environment. The learner is able to train in VR mode even if no physical test bed has been

Figure 10.2 Learning approaches.

built. During the assembly phase, more and more parts are physically available. The missing parts can be placed as hologram overlays, and the learner can perform spatial learning (location of parts) in AR mode.

Additionally, simulation data can be visualized to train already simulated learning situations. During operational phase already performed test runs can be displayed to train learning situations even if the test bed is not in service. In our new learning approach learners can perform learning and training situations in all the three states of the test bed life cycle.

Design of the learning scenario

A learning scenario should always reference a clearly defined objective. In the validation method, the context and the learner influence the design of the learning scenario (Airasian et al., 2001; Bloom, Englehart, Furst, Hill, & Krathwohl, 1956; Starr, Manaris, & Stalvey, 2008; Weidenmann, 1993). The design of such a learning scenario can be compared to the requirements of the engineering phase for product development. There is a need for detailed preexamination and requirements analysis (Meyer, 2003; Ross & Schoman, 1977).

Additionally, aspects of learning psychology have to be considered (Schul- meister, 2004). The learning objective can be separated into knowledge and competences. Knowledge can be grouped into conceptual, procedural and declarative knowledge. Knowledge is the basis for acquiring competences. The acquired competences can be transferred to other domains (Heyse & Erpenbeck, 2004; Hudson & Miller, 2005; North, 2011). We use a context- based approach to support the learner, by showing location-based information of parts, temperature and pressure. Several learning methods use location to help learners remember content. One of these methods is called Loci, a method of memory enhancement using spatial memory to recall information. This method was developed in ancient Rome and Greece to support learning large numbers and texts (Yates, 1966). The Loci method has already been implemented in a mobile app called Loci Spheres. The app has been evaluated in an in-the-wild study. Visual stimuli such as spatial and panning loci provide higher perceived system support (Wieland, Muller, Pfeil, & Reiterer, 2017).