CAREER: Supporting the Intelligibility of Context-Aware Applications

职业：支持上下文感知应用程序的清晰度

• 批准号: 0746428
• 负责人: Anind Dey
• 金额: $ 50万
• 依托单位: Carnegie-Mellon University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-04-15 至 2014-03-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0746428&HistoricalAwards=false
• 关键词: CAREER; Supporting; Intelligibility; Context; Aware

The maturing discipline of ubiquitous computing (ubicomp) has largely ignored usability in lieu the more fundamental quest to simply develop and deploy compelling applications. In these projects usability and social analysis is often post-hoc, one-off, and not accreting into a uniform body of scientific knowledge. This project spotlights the core ubicomp usability challenge as "intelligibility." That is the mapping of mental models, the user's explanations and predictions of a system's expected behavior, to the actual behavior of context-aware ubicomp application. While the use of mental models has been applied to well-understood systems, its application to context-aware systems is novel and promising. It is critical to understand how mental models develop, what sorts of explanations will promote correct mental model formation and what feedback will support users in making correct predictions about application behavior.Broader Impact: The combination of the fundamental understanding gleaned from the proposed studies with the resulting widely disseminated toolkit, has the potential to enable a new class of context aware applications and related usability metrics. Improved usability of and trust in context-aware applications should reduce the chance of abandonment by making it clear how these applications behave. Easier to understand systems may engage a broader cross-section of end users, advancing the adoption of context-aware systems. The PI has demonstrated that ubicomp design and evaluation projects such as this engage undergraduates from groups under-represented in computer science research.

日益成熟的普适计算(Ubiomp)学科在很大程度上忽略了可用性，而不是更根本的追求，即简单地开发和部署引人注目的应用程序。在这些项目中，可用性和社会分析通常是事后的、一次性的，并且不会增加到一个统一的科学知识体系中。这个项目将核心的Ubiomp可用性挑战集中在“可理解性”上。这就是心理模型的映射，即用户对系统预期行为的解释和预测，到上下文感知ubiomp应用程序的实际行为。虽然心理模型的使用已经被应用于人们很好理解的系统，但它在上下文感知系统中的应用是新颖和有前途的。理解心理模型是如何发展的，什么样的解释将促进正确的心理模型的形成，以及什么样的反馈将支持用户对应用程序行为做出正确的预测，这一点至关重要。广泛的影响：从拟议的研究中收集的基本理解与由此产生的广泛传播的工具包的结合，有可能实现一类新的情景感知应用程序和相关的可用性度量。通过明确这些应用程序的行为方式，提高环境感知应用程序的可用性和信任度应该会减少放弃这些应用程序的机会。更容易理解的系统可能会吸引更多的终端用户，从而推动情景感知系统的采用。PI已经证明，像这样的Ubiomp设计和评估项目吸引了来自计算机科学研究中代表性不足群体的本科生。