Environmental User Experience (UX) data collection is essential for user research, enabling evidence-based design decisions. However, traditional retrospective methods like micro-phenomenological interviews suffer from recall inaccuracies and memory distortions. Concurrent UX data collection methods with environmental contexts are promising but lack in-depth investigation. To examine this potential, we conducted a formative study with 34 participants, identifying design goals such as natural interaction, in-situ annotation, and spatial-temporal coupling. We developed JourneyCapturer, an interactive tool that fulfills these goals to integrate concurrent annotation within Immersive Virtual Environment (IVE), enabling real-time UX data capture within contextual scenarios. Using a mixed-method design, we comparatively evaluated concurrent IVE annotations, retrospective interviews, and the combined method with 20 participants, demonstrating how JourneyCapturer improves UX collection processes and outcomes. Our findings suggest that a consciously proactive concurrent IVE method with a first-person perspective advances UX research, offering implications for expert collaboration, multi-modal analytics, and IVE-based field studies.

Immersive Virtual Environment (IVE) have demonstrated substantial potential in user research. However, the integration of IVE into Journey Map (JM) creation remains unexplored, as traditional JM creation still predominantly relies on 2D data. To bridge this research gap, we integrated IVE into the JM creation process, aiming to explore the potential of applying IVE in the JM creation process. Through a formative study with design experts ($\mathrm{N}=14$), six design goals were identified, reflecting designers' expectations for incorporating IVE into JM creation. Based on these goals, we developed ImmerJM, a tool that enables designers to analyze and document user journeys directly within immersive 3D environments. We conducted a comparative evaluation with User Experience (UX) participants ($\mathrm{N}=20$) to assess the JM creation process of ImmerJM against a traditional screen-based method. The quantitative and qualitative analyses revealed key improvements (e.g., phase-based JM mapping) in journey process and outputs. Our findings suggest that ImmerJM advances JM methodologies by leveraging spatial immersion, offering crucial implications for future JM creation and broader UX design practices involving IVE technologies.

Mobile Crowdsensing (MCS), which assigns outsourced sensing tasks to volunteer workers, has become an appealing paradigm to collaboratively collect data from surrounding environments. However, during actual task implementation, various unpredictable disruptions are usually inevitable, which might cause a task execution failure and thus impair the benefit of MCS systems. Practically, via reactively shifting the pre-determined assignment scheme in real time, it is usually impossible to develop reassignment schemes without a sacrifice of the system performance. Against this background, we turn to an alternative solution, i.e., proactively creating a robust task assignment scheme offline. In this work, we provide the first attempt to investigate an important and realisticRoBustTaskAssignment (RBTA) problem in MCS systems, and try to strengthen the assignment scheme's robustness while minimizing the workers’ traveling detour cost simultaneously. By leveraging the workers’ spatiotemporal mobility, we propose an assignment-graph-based approach. First, an assignment graph is constructed to locally model the assignment relationship between the released MCS tasks and available workers. And then, under the framework of evolutionary multi-tasking, we devise a population-based optimization algorithm, namelyEMTRA, to effectively achieve adequate Pareto-optimal schemes. Comprehensive experiments on two real-world datasets clearly validate the effectiveness and applicability of our proposed approach.