相关论文: "Merging Results Is No Easy Task": An Internationa…
User experience (UX) has undergone a revolution in collaborative practices, due to tools that enable quick feedback and continuous collaboration with a varied team across a design's lifecycle. However, it is unclear how this shift in…
Many software companies face challenges in their work with User eXperience (UX) and how to integrate UX practices into existing development processes. A better understanding of these challenges can help researchers and practitioners better…
Usability testing has long been a core interest of HCI research and forms a key element of industry practice. Yet our knowledge of it harbours striking absences. There are few, if any detailed accounts of the contingent, material ways in…
Context: User-Centered Design and Agile methodologies focus on human issues. Nevertheless, agile methodologies focus on contact with contracting customers and generating value for them. Usually, the communication between end users and the…
Cross-disciplinary teams increasingly work with high-dimensional scientific datasets, yet fragmented toolchains and limited support for shared exploration hinder collaboration. Prior immersive visualization and analytics research has…
AI-assisted usability analysis can potentially reduce the time and effort of finding usability problems, yet little is known about how AI's perceived expertise influences evaluators' analytic strategies and perceptions over time. We ran a…
The complexity of UX design practice extends beyond ill-structured design problems to include uncertainties shaped by shifting stakeholder priorities, team dynamics, limited resources, and implementation constraints. While prior research in…
Analyzing usability test videos is arduous. Although recent research showed the promise of AI in assisting with such tasks, it remains largely unknown how AI should be designed to facilitate effective collaboration between user experience…
Developing artificial intelligence (AI) tools for healthcare is a collaborative effort, bringing data scientists, clinicians, patients and other disciplines together. In this paper, we explore the collaborative data practices of research…
Today, the prominence of data science within organizations has given rise to teams of data science workers collaborating on extracting insights from data, as opposed to individual data scientists working alone. However, we still lack a deep…
Human-robot collaboration enables highly adaptive co-working. The variety of resulting workflows makes it difficult to measure metrics as, e.g. makespans or idle times for multiple systems and tasks in a comparable manner. This issue can be…
Stakeholder involvement is one of the major success factors in integrating user experience (UX) practices into software development processes and organizations. It is also a necessity for agile software development. However, practitioners…
How do analysis goals and context affect exploratory data analysis (EDA)? To investigate this question, we conducted semi-structured interviews with 18 data analysts. We characterize common exploration goals: profiling (assessing data…
One of the key components of designing usable and useful collaborative information retrieval systems is to understand the needs of the users of these systems. Our research team has been exploring collaborative information behavior in a…
UX practitioners face novel challenges when designing user interfaces for machine learning (ML)-enabled applications. Interactive ML paradigms, like AutoML and interactive machine teaching, lower the barrier for non-expert end users to…
Data exploration is an important aspect of the workflow of mixed-methods researchers, who conduct both qualitative and quantitative analysis. However, there currently exists few tools that adequately support both types of analysis…
This paper proposes an interpretable non-model sharing collaborative data analysis method as one of the federated learning systems, which is an emerging technology to analyze distributed data. Analyzing distributed data is essential in many…
The complexity of exploratory data analysis poses significant challenges for collaboration and effective communication of analytic workflows. Automated methods can alleviate these challenges by summarizing workflows into more interpretable…
Immersive analytics has the potential to promote collaboration in machine learning (ML). This is desired due to the specific characteristics of ML modeling in practice, namely the complexity of ML, the interdisciplinary approach in…
The development of digital humanities necessitates scholars to adopt more data-intensive methods and engage in multidisciplinary collaborations. Understanding their collaborative data behaviors becomes essential for providing more curated…