Deadline: December 16th, 2016
Format: 4 pages in the SIGCHI Extended Abstracts Format
Submit: Email submissions to Casey Dugan, cadugan(at)us.ibm.com
We are soliciting 4-page position papers, from researchers across both industry and academia. A description of the authors’ hands-on experience of explicitly designing for user awareness or incorporating awareness mechanisms into their own research is expected. Further, practical advice on what worked well (or what went very badly!), given those decisions, will form the basis of an interesting position paper on the topic. These positions will be shared and debated in the workshop.
We invite submissions from those who have researched:
- Applications or interfaces incorporating awareness mechanisms: such as social media, educational interfaces, eCommerce,persuasion, & visualization
- Interfaces specifically designed for a certain type of awareness or with a focus on an awareness technique or problem, such as information overload, ambient displays, alerting mechanisms, urgency widgets, visualization (& other) dashboards, and assisted browsing
- Machine learning or other algorithms to support filtering to focus awareness or making users aware of new items, such as with recommender systems
More about the topic:
Awareness is a key user interface and interaction paradigm. Choosing what to make the user aware of, at what time, and how to evoke awareness, has a critical impact on system usage and perception. Human attention is limited and information overload is a common problem facing users. As such, many different kinds of systems, such as email, educational interfaces, calendars, and social networks have benefited from designing for awareness. Various mechanisms and types of interfaces have specifically been designed to support and evoke user awareness, such as visualization dashboards, ambient displays, and alerts. Timing is a critical dimension of awareness, and urgency widgets, such as those employed by booking.com, are an example of making users aware of the most important information at the current moment.
Behind such interfaces, machine learning and other algorithms can be applied to filter information to focus a user’s awareness or highlight information that may be missed or that may go unseen. Recommender systems have a history of being used to make users more aware of content and other items they might not have otherwise seen. For example, recommenders have made users aware of new neighborhoods and traffic in cities. With the large amount of content shared on social media, interactive visualizations can also be used to support user awareness of others’ content, such as bookmarks. In social networks, they can be used to recommend known, offline colleagues they were unaware were currently using the site.