Facebook Reality Labs*Lead co-authors
The not-too-distant future may bring more ubiquitous personal computing technologies seamlessly integrated into people's lives, with the potential to augment reality and support human cognition. For such technology to be truly assistive to people, it must be context-aware. Human experience of context is complex, and so the early development of this technology benefits from a collaborative and interdisciplinary approach to research — what the authors call “hybrid methodology” — that combines (and challenges) the frameworks, approaches, and methods of machine learning, cognitive science, and anthropology. Hybrid methodology suggests new value ethnography can offer, but also new ways ethnographers should adapt their methodologies, deliverables, and ways of collaborating for impact in this space. This paper outlines a few of the data collection and analysis approaches emerging from hybrid methodology, and learnings about impact and team collaboration,...
University of Edinburgh
University of Edinburgh
A key challenge in carrying out product design research is obtaining rich contextual information about use in the wild. We present a method that algorithmically mediates between participants, researchers, and objects in order to enable real-time collaborative sensemaking. It facilitates contextual inquiry, revealing behaviours and motivations that frame product use in the wild. In particular, we are interested in developing a practice of use driven design, where products become research tools that generate design insights grounded in user experiences. The value of this method was explored through the deployment of a collection of Bluetooth speakers that capture and stream live data to remote but co-present researchers about their movement and operation. Researchers monitored a visualisation of the real-time data to build up a picture...
by KATHY BAXTER, Google
EPIC2014 Workshop by Anna Avrekh, Kathy Baxter, & Bob Evans
At the EPIC 2013 Keynote, Tricia Wang observed that, if you are not working with “Big Data,” the implication is that your data are “small.” Although the number of data points or participants may not be in the millions or ever thousands, the data we gather is actually far richer. As our community knows, web analytics or logs can tell us WHAT people are doing but never WHY. We may attempt to infer it based on what we see but unless we ask our users why they are doing something that we have recorded (with or without their knowledge), we can never know for sure.
Later in the conference, I hosted a Salon on “Big Data” with discussants Jens Riegelsberger (Google) and Todd Cherkasky (SapientNitro). The interest in the salon far exceeded the space available. One key theme that emerged was a desire to learn how to incorporate “Big Data” into their work. Few of the participants had the means to pull logs and do deep statistical analysis...