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This paper introduces the concept of “news informatics” to refer to journalistic presentation of big data in online sites. For users to be engaged with such data-driven public information, it is important to incorporate interactive tools so that each person can extract personally relevant information. Drawing upon a communication model of interactivity, we designed a data-rich site with three different types of interactive features—namely, modality interactivity, message interactivity, and source interactivity—and empirically tested their relative and combined effects on user engagement and user experience with a 2 (modality) × 3 (source) × 2 (message) field experiment (N =166). Findings shed light on how interface designers, online news editors and journalists can maximize user engagement with data-rich news content. Certain interactivity combinations are found to be better than others, with a structural equation model (SEM) revealing the underlying theoretical mechanisms and providing implications for the design of news informatics.
Analysis is a key part of usability testing where UX practitioners seek to identify usability problems and generate redesign suggestions. Although previous research reported how analysis was conducted, the findings were typically focused on individual analysis or based on a small number of professionals in specific geographic regions. We conducted an online international survey of 279 UX practitioners on their practices and challenges while collaborating during data analysis. We found that UX practitioners were often under time pressure to conduct analysis and adopted three modes of collaboration: independently analyze different portions of the data and then collaborate, collaboratively analyze the session with little or no independent analysis, and independently analyze the same set of data and then collaborate. Moreover, most encountered challenges related to lack of resources, disagreements with colleagues regarding usability problems, and difficulty merging analysis from multiple practitioners. We discuss design implications to better support collaborative data analysis.
Sentence completion, originally a semi-projective psychological technique, has been used as an effective and lightweight user research method in user experience (UX) design. More information is yet still needed to understand how different sentence stems probe users’ insights, thereby providing recommendations for effective sentence completion surveys. We used the completion method on a large-scale sample to explore (e-)readers’ experiences and needs. Depending on their reading habits, participants (N=1880) were asked to complete a set of sentences, as part of a web survey. With 14143 user ideas collected in two weeks, our results confirm that remote online sentence completion is a cost-effective data collection method able to uncover feelings, attitudes, motivations, needs, or frustrations. Variation in sentence stems affected collected data in terms of item response rate, idea quantity as well as variety and originality. Building on previous research, this paper delivers actionable insights to optimize the richness of sentence completion outputs.
For many students, attending college is a dramatic but necessary change. To gain a better understanding of experiences that are unique to autistic college students, we conducted a mixed-method study with 20 students (10 autistic and 10 neurotypical). We collected physiological, contextual, experience, and environmental data from their natural environment using Fitbit and smartphones. We found that stress patterns, emotional states, and physical states are similar for both groups. Our autistic participants prioritized academic success over everything else, often intentionally confining their movements among academic, resident, and work locations to engage themselves with academic work as much as possible. They had a small number of friends, always preferring quality over quantity and sometimes regarding friends as close as family members. To maintain a better social life, they extensively used social media. They slept more than neurotypical participants per day; however, they experienced lower sleep quality.
Voice assistants (VAs) are present in homes, smartphones, and cars. They allow users to perform tasks without graphical or tactile user interfaces, as they are designed for natural language interaction. However, we found that currently, VAs are emulating human behavior by responding in complete sentences, limiting the design options, and preventing VAs from meeting their full potential as a utilitarian tool. We implemented a VA that handles requests in three response styles: two differing short keyword-based response styles and a full-sentence baseline. In a user study, 72 participants interacted with our VA by issuing eight requests. Results show that the short responses were perceived similarly useful and likable while being perceived as more efficient, especially for commands, and sometimes better to comprehend than the baseline. To achieve widespread adoption, we argue that VAs should be customizable and adapt to users instead of always responding in full sentences.