数据驱动的人为因素评估拟议框架

Isabelle Ormerod, H. Dybvik, Mike Fraser, Chris Snider
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引用次数: 0

摘要

设计周期中以人为中心的方法对于提高产品的可用性和包容性至关重要。然而,传统人为因素评估的定性性质可能会造成瓶颈,因此需要更多的数据驱动方法。本文提出了一个数据驱动的人为因素框架,旨在整合混合方法。案例研究说明了该框架在现实世界中的应用,并总结了所面临的挑战,呼吁采用可靠的数据收集方法、平衡混合方法、需要可解释的系统以及跨学科的专业知识。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A proposed framework for data-driven human factors evaluation
Human-centred approaches within the design cycle are crucial to enhance usability and inclusivity of products. However, the qualitative nature of traditional human factors evaluation can create bottle necks, prompting the need for more data driven methods. A framework for data-driven human factors is presented, looking to integrate mixed-method approaches. Case studies illustrate its usage in real-world scenarios and challenges are summarised, calling for robust data collection methods, balancing of mixed methods, a need for explainable systems, and interdisciplinary expertise.
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