Impacts of Adaptive Contextual Immersive Environments in ProxSituated Data Analytics
This research project investigates how different data analytics environments influence users’ memory recall and cognitive performance in industrial contexts. As modern industrial systems increasingly rely on complex data dashboards to monitor machine performance and operational status, there is a growing need to understand how interface design and interaction modalities affect users’ ability to interpret, retain, and act on critical information. In particular, the project explores whether immersive environments—such as virtual reality (VR)—can enhance memory recall and situational awareness compared to more traditional, non-immersive analytics interfaces.
The study is conducted in a controlled laboratory setting at the Embodied Visualisation Lab and adopts a within-subject experimental design. Participants engage with simulated industrial scenarios where they analyse machine performance data presented through visual dashboards. These dashboards include key operational metrics such as availability, quality, runtime, and indicators of abnormal events (e.g., faults or breakdowns). By embedding these tasks within immersive environments, the study aims to examine how spatial context, embodiment, and interaction influence users’ cognitive processing of complex data.
Participants complete the study using a Meta Quest 3 VR headset, interacting with data visualisations in virtual industrial settings designed to mimic real-world operational environments. The experimental protocol consists of four conditions, each completed in separate sessions approximately two days apart to minimise learning effects and fatigue. Each session lasts around 60 minutes and follows a structured sequence of tasks designed to assess memory performance under different conditions.
Each session includes three key phases: a learning phase, a distraction phase, and a recall phase. During the learning phase, participants explore the data dashboards and are encouraged to understand system performance and identify key patterns or anomalies. This is followed by a short distraction task to reduce short-term memory effects. In the recall phase, participants answer a series of questions assessing their ability to remember specific details, trends, and events from the dashboards. This design enables the study to systematically evaluate how different environments affect both immediate and delayed recall.
In addition to task performance, the study collects subjective and qualitative data through questionnaires and short interviews. These measures capture participants’ perceived workload, usability, and sense of immersion, providing a more comprehensive understanding of how different environments influence both cognitive and experiential aspects of data analytics. By combining behavioural, subjective, and qualitative insights, the project aims to identify design principles for more effective data analytics systems.
The findings of this research are expected to contribute to the design of next-generation immersive analytics tools for industrial applications. By understanding how immersive environments shape memory and cognition, the project aims to inform the development of systems that better support decision-making, reduce cognitive load, and improve the reliability of human performance in complex, data-rich environments.
