Automotive user interface (AUI) evaluation becomes increasingly complex due to novel interaction modalities, driving automation, heterogeneous data, and dynamic environmental contexts. Immersive analytics may enable efficient explorations of the resulting multilayered interplay between humans, vehicles, and the environment. However, no such tool exists for the automotive domain. With AutoVis, we address this gap by combining a non-immersive desktop with a virtual reality view enabling mixed-immersive analysis of AUIs. We identify design requirements based on an analysis of AUI research and domain expert interviews (N=5). AutoVis supports analyzing passenger behavior, physiology, spatial interaction, and events in a replicated study environment using avatars, trajectories, and heatmaps. We apply context portals and driving-path events as automotive-specific visualizations. To validate AutoVis against real-world analysis tasks, we implemented a prototype, conducted heuristic walkthroughs using authentic data from a case study and public datasets, and leveraged a real vehicle in the analysis process.

Pair-learning is beneficial for learning outcome, motivation, and social presence, and so is virtual reality (VR) by increasing immersion, engagement, motivation, and interest of students. Nevertheless, there is a research gap if the benefits of pair-learning and VR can be combined. Furthermore, it is not clear which influence it has if only one or both peers use VR. To investigate these aspects, we implemented two types of VR pair-learning systems, a symmetric system with both peers using VR and an asymmetric system with one using a tablet. In a user study (N=46), the symmetric system statistically significantly provided higher presence, immersion, player experience, and lower intrinsic cognitive load, which are all important for learning. Symmetric and asymmetric systems performed equally well regarding learning outcome, highlighting that both are valuable learning systems. We used these findings to define guidelines on how to design co-located VR pair-learning applications, including characteristics for symmetric and asymmetric systems.