Crowdsourcing interface feature design with Bayesian optimization

Designing novel interfaces is challenging. Designers typically rely on experience or subjective judgment in the absence of analytical or objective means for selecting interface parameters. We demonstrate Bayesian optimization as an efficient tool for objective interface feature refinement. Specifically, we show that crowdsourcing paired with Bayesian optimization can rapidly and effectively assist interface design across diverse deployment environments. Experiment 1 evaluates the approach on a familiar 2D interface design problem: a map search and review use case. Adding a degree of complexity, Experiment 2 extends Experiment 1 by switching the deployment environment to mobile-based virtual reality. The approach is then demonstrated as a case study for a fundamentally new and unfamiliar interaction design problem: web-based augmented reality. Finally, we show how the model generated as an outcome of the refinement process can be used for user simulation and queried to deliver various design insights

Crowdsourcing design guidance for contextual adaptation of text content in augmented reality

Funding Information: This work was supported by EPSRC (grants EP/R004471/1 and EP/S027432/1). Supporting data for this publication is available at https://doi.org/10.17863/CAM.62931.Augmented Reality (AR) can deliver engaging user experiences that seamlessly meld virtual content with the physical environment. However, building such experiences is challenging due to the developer's inability to assess how uncontrolled deployment contexts may infuence the user experience. To address this issue, we demonstrate a method for rapidly conducting AR experiments and real-world data collection in the user's own physical environment using a privacy-conscious mobile web application. The approach leverages the large number of distinct user contexts accessible through crowdsourcing to efciently source diverse context and perceptual preference data. The insights gathered through this method complement emerging design guidance and sample-limited lab-based studies. The utility of the method is illustrated by reexamining the design challenge of adapting AR text content to the user's environment. Finally, we demonstrate how gathered design insight can be operationalized to provide adaptive text content functionality in an AR headset.Publisher PD

Ethnographically-informed distributed participatory design framework for sociotechnical change : co-designing a collaborative training tool to support real-time collaborative writing

Although Wikipedia’s immense success is partially due to its support of the asynchronous collaboration model, researchers argue that the bureaucratic rules and technical infrastructure enabling it feed into Wikipedia’s content bias. Attempts to introduce different collaboration models have so far failed, but the fact that they have occurred persistently over time suggests that at least part of the Wikipedia community favours incorporating features such as real-time collaborative editing. My research is founded on the argument that the advantageous aspects of the asynchronous model should be preserved, although the existing model needs to be complemented by real-time collaboration in settings such as Wikipedia training events. This thesis describes a Participatory Design process resulting in a prototype called WikiSync, a system that introduces real-time collaboration for the Wikipedia community using a responsible design approach that is respectful of Wikipedia’s rich social structure and history. Furthermore, my research has produced an adaptive methodology for co-designing sociotechnical solutions in a geographically distributed community. After an in-depth observation of online Wikipedia training and the existing community innovation processes, my participatory design sessions have helped create a mutual learning environment for co-designing WikiSync in tandem with the community, while addressing a wide range of their concerns about real-time collaboration. I also consulted the broader Wikipedia community using an online social ideation and voting tool to evaluate the desirability and applicability of the solution. Finally, the resulting ethnographically-informed distributed Participatory Design framework provides an innovation process for involving a diverse, widely distributed online community in co-designing sociotechnical solutions

Crowdsourcing interface feature design with Bayesian optimization

Designing novel interfaces is challenging. Designers typically rely on experience or subjective judgment in the absence of analytical or objective means for selecting interface parameters. We demonstrate Bayesian optimization as an efficient tool for objective interface feature refinement. Specifically, we show that crowdsourcing paired with Bayesian optimization can rapidly and effectively assist interface design across diverse deployment environments. Experiment 1 evaluates the approach on a familiar 2D interface design problem: a map search and review use case. Adding a degree of complexity, Experiment 2 extends Experiment 1 by switching the deployment environment to mobile-based virtual reality. The approach is then demonstrated as a case study for a fundamentally new and unfamiliar interaction design problem: web-based augmented reality. Finally, we show how the model generated as an outcome of the refinement process can be used for user simulation and queried to deliver various design insights

Crowdsourcing interface feature design with Bayesian optimization

Designing novel interfaces is challenging. Designers typically rely on experience or subjective judgment in the absence of analytical or objective means for selecting interface parameters. We demonstrate Bayesian optimization as an efficient tool for objective interface feature refinement. Specifically, we show that crowdsourcing paired with Bayesian optimization can rapidly and effectively assist interface design across diverse deployment environments. Experiment 1 evaluates the approach on a familiar 2D interface design problem: a map search and review use case. Adding a degree of complexity, Experiment 2 extends Experiment 1 by switching the deployment environment to mobile-based virtual reality. The approach is then demonstrated as a case study for a fundamentally new and unfamiliar interaction design problem: web-based augmented reality. Finally, we show how the model generated as an outcome of the refinement process can be used for user simulation and queried to deliver various design insights.</p

Research data supporting "Crowdsourcing Interface Feature Design with Bayesian Optimization"

Participant performance data corresponding to experiments described in "Crowdsourcing Interface Feature Design with Bayesian Optimization". File contains separate sheets for each experiment and the design case study. Completion times and comparative rating scores are reported for each design candidate sampled. See publication for detailed descriptions of conditions and metrics.This work was supported by EPSRC (grant EP/R004471/1) and the Trimble Fund

Research data supporting "Crowdsourcing Interface Feature Design with Bayesian Optimization"

Participant performance data corresponding to experiments described in "Crowdsourcing Interface Feature Design with Bayesian Optimization". File contains separate sheets for each experiment and the design case study. Completion times and comparative rating scores are reported for each design candidate sampled. See publication for detailed descriptions of conditions and metrics

Research data supporting "Crowdsourcing Interface Feature Design with Bayesian Optimization"

Participant performance data corresponding to experiments described in "Crowdsourcing Interface Feature Design with Bayesian Optimization". File contains separate sheets for each experiment and the design case study. Completion times and comparative rating scores are reported for each design candidate sampled. See publication for detailed descriptions of conditions and metrics