Using virtual reality as a platform for developing mental models of industrial systems
To effectively design, build, and interact with industrial systems, engineering and technology students must come prepared with a robust understanding of industrial systems. Developing proper understanding of industrial system is complex and daunting process. To understand industrial systems, students must develop mental models for systems; mental models are dynamic, mental representations of what users know and how they perceive the real world around them. Research on mental models of systems distinguish among four notions of mental models. These notions are Device Topology, a notion representing the level of understanding of the structure of a device or a system, usually comprised of individual components; the Envisioning, a notion representing the level of understanding of the components’ function in the device or system out of context; the Causal Model, a notion representing the level of understanding the device’s or system’s purpose or overall function; and, the Simulation, a notion representing the level of understanding how the device or system behaves under specified conditions. In this research, the notion of Device topology was divided to two sub-notions to better reflect on the complexity of industrial systems in compassion to devices. These sub notions were titled Process System topology, a notion representing components in a system that affect the process, and Service System topology, a notion representing components that are servicing the process components.
In recent years, virtual reality (VR) became accessible and attracted interest as a potential learning tool. However, the extent to which VR contributes to positive and enhanced learning remained inconclusive. Multiple studies found that, despite offering higher proper sense of presence, learning outcomes with VR was worse than the learning outcomes with non-immersive technologies; other studies reported that VR provided enhanced learning outcomes in comparison to non-VR instruction. Studies on VR-based learning focused on various topic domains, which seems to hinder reaching generalizable conclusions on the merit of learning in VR.
This thesis pursues two overarching research questions: (1) can interacting, designing, and building systems with interactive VR applications enhance students’ mental models of industrial systems; and, (2) Can level of presence predict level of notions of mental models of systems. The two overarching research questions were evaluated using two VR applications titled Cooling Water Virtual Reality (CWVR) and System Designer VR (SDVR). Engineering and technology students participated in experiments with CWVR and SDVR. Students were instructed to explore a prefabricated cooling water system with CWVR and to design and build an industrial system based on task specifications with SDVR. The results demonstrated that students that began with designing and building a system with SDVR and then interacted with the prefabricated CWVR had a modestly higher levels of notions of Process and Service System Topology and a significantly higher notion of Causal Model of the cooling water system in CWVR, in comparison to students that interacted with the prefabricated CWVR without previous experience with SDVR. The results also demonstrated that presence was significantly associated with the Service System Topology notion of mental model but not with other notions. There were other significant relationships among interactivity parameters with the various notions; however, a review of these results led to only a causal explanation for the relationships with presence.
The conclusions offer that, potentially, a single engagement with system resulted with a modest shift in the notions of mental models of systems, and that an extensive engagement with an application such as SVDR may results in overall significant elevated levels on all notions. Further, the lack of significant relationships between presence and notions of mental models, other than with Service System Topology, may be explained by the overall number of participants with high-level presence.