Collocated collaboration remains a preferred mode of working together, yet current systems often depend on users to create and configure shared content. This places individual effort and attention on managing workspaces rather than engaging in collective sensemaking. We present a mixed-initiative interaction mechanism that automatically transforms ongoing conversation into manipulable shared materials, shifting externalisation from an explicit individual task to a by-product of discourse. We implement this mechanism in CollEagle, an interactive tabletop system that continuously generates candidate content from speech and enables users to curate, organise, and repurpose it for joint activity. A user study shows that automating material production reduces configuration work, supports group coordination, and foregrounds shared meaning-making. We contribute (1) a concrete implementation of a mixed-initiative interaction mechanism for collocated collaboration, and (2) design insights for systems that integrate implicit automation with explicit manipulation to better support joint work.

Office sensor systems often fail to present environmental data related to office well-being in ways that are accessible and comprehensible for non-expert users. To address this, we introduce Situated Data Sensemaking, a concept that enhances individuals’ ability to understand and use data by embedding it within their physical environment. Implemented through OfficeSense, a system that physicalizes environmental parameters—light levels, sound, air quality (CO2), and temperature—within office spaces, this concept was evaluated in a 4-week study (N=11) including a baseline week. OfficeSense influenced perceived data literacy and understanding of office well-being. Findings indicate that Situated Data Sensemaking supported participants in aligning their perceptions of environmental conditions with real-time data, influenced by spatial and temporal contexts, personal preferences, and collaborative interactions. This study demonstrates that embedding data in shared physical spaces can enhance users’ confidence in interpreting environmental data and foster collective awareness of workplace conditions.

Sensors in offices mainly measure environmental data, missing qualitative insights into office workers’ perceptions. This opens the opportunity for active individual participation in data collection. To promote reflection on office well-being while overcoming experience sampling challenges in terms of privacy, notification, and display overload, and in-the-moment data collection, we developed Click-IO. Click-IO is a tangible, privacy-sensitive, mobile experience sampling tool that collects contextual information. We evaluated Click-IO for 20-days. The system enabled real-time reflections for office workers, promoting self-awareness of their environment and well-being. Its non-digital design ensured privacy-sensitive feedback collection, while its mobility facilitated in-the-moment feedback. Based on our findings, we identify design recommendations for the development of mobile experience sampling tools. Moreover, the integration of contextual data with environmental sensor data presented a more comprehensive understanding of individuals’ experiences. This research contributes to the development of experience sampling tools and sensor integration for understanding office well-being.

While data is the cornerstone of modern design strategies, design researchers frequently struggle when performing data work. This creates a need to design tools that enable design researchers to actively engage with data. However, this presupposes understanding how design researchers create meaning from data representations, as the way of visualizing the data, along with other factors, can significantly impact the extracted insights, increasing uncertainty about the quality of the outcome. As a response to this problem, we explore how design researchers make sense of data in a case study: making sense of paired subjective and objective sleep and stress data visualizations. By synthesizing our findings from two user studies, we construct a sensemaking model which highlights how uncertainty related to data qualities, visualization parameters, and the viewer's background affects the insight-generation process. Our findings have implications for the future development of tools and techniques for visual data sensemaking for designers.

Understanding the way that the urban environment affects the human body is important for the advancement of urban health and wellbeing. Wearable technologies can be used to collect data related to physiological responses, such as electrodermal activity (EDA). However, it is difficult to interpret data collected in the wild without contextual information. Previous studies in the urban domain have also ignored the effect of exercise as a stressor. This study emphasizes the possible effects of movement on physiological responses during outdoor walking, and presents a methodology for analyzing physiological responses based on activity data and freely available contextual data from OpenStreetMap. The methodology is applied to analyze the physiological responses of walkers while navigating an outdoor route in Sydney, Australia. Statistical analysis is conducted to identify the effect of selected urban features and movement on physiological responses. The results suggest that the duration and intensity of activity, as well as the density of mixed-use affect physiological responses significantly.