Emu:用户研究的用户粘性模型。

Bo-Jhang Ho, Nima Nikzad, Bharathan Balaji, Mani Srivastava
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引用次数: 2

摘要

移动技术推动即时生态评估和干预,为用户行为和慢性病管理提供了前所未有的视角。这些方法的成功取决于在适当的时候吸引用户,从而最大限度地提高问卷和任务的完成率。然而,移动操作系统在精确指定通知和吸引用户的上下文条件方面提供的支持很少,研究设计师往往缺乏自己构建上下文感知软件的专业知识。为了解决这个问题,我们开发了Emu,这是一个框架,通过为任务通知指定时间和上下文约束提供简洁而强大的接口,简化了上下文感知学习应用程序的开发。在本文中,我们介绍了Emu API的设计,并演示了它在捕获基于智能手机的学习应用程序的一系列常见场景中的使用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Emu: Engagement Modeling for User Studies.

Emu: Engagement Modeling for User Studies.

Emu: Engagement Modeling for User Studies.

Emu: Engagement Modeling for User Studies.

Mobile technologies that drive just-in-time ecological momentary assessments and interventions provide an unprecedented view into user behaviors and opportunities to manage chronic conditions. The success of these methods rely on engaging the user at the appropriate moment, so as to maximize questionnaire and task completion rates. However, mobile operating systems provide little support to precisely specify the contextual conditions in which to notify and engage the user, and study designers often lack the expertise to build context-aware software themselves. To address this problem, we have developed Emu, a framework that eases the development of context-aware study applications by providing a concise and powerful interface for specifying temporal- and contextual-constraints for task notifications. In this paper we present the design of the Emu API and demonstrate its use in capturing a range of scenarios common to smartphone-based study applications.

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