Distributed interaction design

Abstract

Creating user interfaces that are natural, guessable, learnable, memorable and accessible is a persistent challenge.Involving end users in the design process is a well-established approach to address these challenges, but traditional participatory design has limitations, especially when it comes to scaling beyond the lab and reaching diverse participants. I build on the success of a popular participatory design method called end-user elicitation. Elicitation studies work by presenting the effect of an interaction (e.g., what happens after a user makes a gesture) and asking end-user participants to perform the action that would have caused that effect (e.g., the gesture itself). Despite their success, elicitation studies have important limitations. They typically are confined to a lab setting, limiting the number of participants, their diversity and the representativeness of the results. Also, analyzing the studies' results is a laborious process. Furthermore, elicitation studies lack a formal approach to evaluate the quality of their results. My work addresses these limitations by scaling beyond the lab and conducting distributed elicitation studies with online crowds. In this dissertation, I have created an open platform and formulated the Distributed Interaction Design (DXD) process. This work overall contributes methodological extensions to elicitation studies, an open platform for the research community, and empirical studies of user-generated interactions such as gestures, voice commands, and icons. The thesis I demonstrate in this work is:Using a custom-built platform to conduct Distributed Interaction Design (DXD) enables: creating user-elicited interactions; evaluating the guessability, learnability, and memorability of interaction designs; and the recruitment of participants through third-party services in a timely manner.

Designing Gestures for Data Exploration with Public Displays via Identification Studies

“…In our study, we found that the participants were proficient in using both systems, successfully discovering the majority of the gestures required to activate the system functions. In summary, whereas the prior work used identification studies to evaluate the results of elicitation studies [4,15], we show that identification studies can be used as an alternative to elicitation studies to craft gesture-based interfaces, enabling the interaction designers to harness the advantages of in situ observations, as well as the cost-effectiveness and speed [16] offered by crowd-sourcing platforms.…”

“…This allows the researchers to collect the symbols most preferred by their potential user base; the overarching idea is that such symbols are preferable to those created by HCI professionals alone [24]. However, the traditional in-lab elicitation studies face limitations related to their sample populations; these are often composed of potential users available on university campuses, which may not be representative of a wide range of users [4,15]. To overcome these limitations, Ali et al [4] proposed conducting elicitation studies using a custom-made crowd-sourcing platform and validated this idea on a set of user-generated voice-based commands.…”

“…Crowdlicit by Ali et al [4] introduced a technique called end-user identification studies, in which the participants are asked to match the symbolic input to referents, reversing the process of an elicitation study, illustrated in Figure 2 [4,10]. Identification studies can be conducted online, which addresses some of the limitations of elicitation studies regarding the available subjects and resources needed [4,15]. They do come with their own limitations compared to elicitation studies, such as a lack of physical presence and performance of the gestures in front of the interface, which we address further in this paper.…”

“…They do come with their own limitations compared to elicitation studies, such as a lack of physical presence and performance of the gestures in front of the interface, which we address further in this paper. Previously, they were only used to evaluate the user-generated gesture sets created with elicitation studies [4,15]. In this paper, we build on this idea and show how identification studies can be combined with in situ observations and used as a design method for gesture-based, interactive data visualizations.…”