Gathering public opinions, such as surveys, at events typically requires approaching people in situ, but this can disrupt the positive experience they are having and can result in very low response rates. As an alternative approach, we present the design and implementation of VoxBox, a tangible system for gathering opinions on a range of topics in situ at an event through playful and engaging interaction. We discuss the design principles we employed in the creation of VoxBox and show how they encouraged wider participation, by grouping similar questions, encouraging completion, gathering answers to open and closed questions, and connecting answers and results. We evaluate these principles through observations from an initial deployment and discuss how successfully these were implemented in the design of VoxBox.
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How can civic technology be designed to encourage more public engagement? What new methods of data collection and sharing can be used to engender a different relationship between citizens and the state? One approach has been to design physical systems that draw people in and which they can trust, leading them to give their views, opinions or other data. So far, they have been largely used to elicit feedback or votes for one or two questions about a given topic. Here, we describe a physical system, called Sens-Us, which was designed to ask a range of questions about personal and sensitive information, within the context of rethinking the UK Census. An in-the-wild study of its deployment in a city cultural center showed how a diversity of people approached, answered and compared the data that had been collected about themselves with others. We discuss the findings in relation to the pros and cons of using this kind of innovative technology when wanting to promote civic engagement or other forms of public engagement.
Many companies would like to redesign their workspaces to make them more pleasant and even fun places to work in. An assumption is it will result in social and economic benefits. However, it can be difficult to achieve because of cost, level of disruption and regulations. We present an alternative approach that provides an injection of playfulness into "drab" office buildings. A lightweight technology intervention was designed -Mood Squeezerthat asks people to reflect on their mood by squeezing a colored ball from a box set. The squeezes are mirrored back as an aggregate colorful visualization on a public floor display. An in-the-wild study showed how this intervention was successful at getting people to squeeze their mood, leading to a diversity of conversations throughout the building. We discuss how this lightweight approach to office augmentation can provide new opportunities for opening up a "closed" workplace.
Personalization mechanisms often employ behavior monitoring and machine learning techniques to aid the user in the creation and management of a preference set that is used to drive the adaptation of environments and resources in line with individual user needs. This article reviews several of the personalization solutions provided to date and proposes two hypotheses: (A) an incremental machine learning approach is better suited to the preference learning problem as opposed to the commonly employed batch learning techniques, (B) temporal data related to the duration that user context states and preference settings endure is a beneficial input to a preference learning solution. These two hypotheses are the cornerstones of the Dynamic Incremental Associative Neural NEtwork (DIANNE) developed as a tailored solution to preference learning in a pervasive environment. DIANNE has been evaluated in two ways: first, by applying it to benchmark datasets to test DIANNE's performance and scalability as a machine learning solution; second, by end-users in live trials to determine the validity of the proposed hypotheses and to evaluate DIANNE's utility as a preference learning solution.
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