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User-Test Results Injection into Task-Based Design Process for the Assessment and Improvement of Both Usability and User Experience

机译:将用户测试结果注入基于任务的设计过程中,以评估和改善可用性和用户体验

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摘要

User Centered Design processes argue for user testing in order to assess and improve the quality of the interactive systems developed. The underlying belief is that the findings from user testing related to usability and user experience will inform the design of the interactive system in a relevant manner. Unfortunately reports from the industrial practice indicate that this is not straightforward and a lot of data gathered during user tests is hard to understand and exploit. This paper claims that injecting results from user-tests in user-tasks descriptions support the exploitation of user test results for designing the n+1 prototype. In order to do so, the paper proposes a set of extensions to current task description techniques and a process for systematically populating task models with data and analysis gathered during user testing. Beyond the already known advantages of task models, these enriched task models provide additional benefits in different phases of the development process. For instance, it is possible to go beyond standard task-model based performance evaluation exploiting real performance data from usability evaluation. Additionally, it also supports task-model based comparisons of two alternative systems. It can also support performance prediction and overall supports identification of usability problems and identifies shortcomings for user experience. The application of such a process is demonstrated on a case study from the interactive television domain.
机译:以用户为中心的设计过程要求进行用户测试,以便评估和提高所开发的交互式系统的质量。基本的信念是,来自用户测试的与可用性和用户体验有关的发现将以相关的方式为交互式系统的设计提供信息。不幸的是,来自工业实践的报告表明,这并非一帆风顺,并且在用户测试期间收集的许多数据难以理解和利用。本文声称,将来自用户测试的结果注入到用户任务描述中,可以支持利用用户测试结果来设计n + 1原型。为了做到这一点,本文提出了对当前任务描述技术的一组扩展,以及使用用户测试期间收集的数据和分析系统地填充任务模型的过程。除了任务模型的已知优势之外,这些丰富的任务模型还在开发过程的不同阶段提供了其他好处。例如,有可能超越基于标准任务模型的性能评估,从而利用可用性评估中的实际性能数据。此外,它还支持基于任务模型的两个替代系统的比较。它还可以支持性能预测,并总体上支持识别可用性问题并识别用户体验的不足。在来自交互式电视领域的案例研究中证明了这种方法的应用。

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