EAGER: AnalyzeD - Analyzing Engineering Design Activities

这个早期概念探索性研究奖章的研究目标是对工程设计行为(实际工程活动)、解决问题的偏好(个人心理倾向)和工程师的实时生理反应(脑电、心电和其他生理遥测数据)之间的关系进行定量测量、建模和理解。它将产生一个工程设计测量系统，通过减少基于个人行为的不确定性以及支持设计团队的组建和优化的数据，帮助改进决策分析模型，使人们能够对工程设计人员与其上下文环境(例如，计算和协作工具、空间和机器)之间的交互有新的见解。这项研究将促进分析创造力和结构化工程方法与发散快速原型的非结构化创造力的整合，使设计团队和技术组织能够提高其产品/系统开发和设计过程的转换水平、速度和价值。如果成功，本研究结果将提供对工程设计人员(单独和团队)决策时潜在认知过程的洞察，包括基于设计活动类型的不同不确定性下经历的压力大小与个人解决问题偏好之间的关系。结果将有助于开发新的支持性工具和环境，以协助解决复杂的工程挑战，同时考虑到工程师的个人心理倾向和设计活动的具体发散或融合性质。这一知识将有助于在不同的工程设计阶段和趋同的工程设计阶段之间进行智力转换，并最大限度地提高每个工程师在两个阶段的参与和产出。

The research objective of this EArly concept Grant for Exploratory Research (EAGER) award is to quantitatively measure, model, and understand the relationships between engineering design behavior (actual engineering activity), problem solving preference (individual psychological predisposition), and real-time physiological responses of engineers (EEG, ECG, and other physiology telemetry data). It will result in an engineering design measurement system that will help improve decision analysis models by reducing individual behavior-based uncertainty, as well as data that will support the formation and optimization of design teams, enabling new insights into the interactions between engineering designers and their contextual environments (e.g., computational and collaborative tools, spaces, and machines). This research will facilitate the integration of analytical creativity and structured engineering approaches with the less structured creativity of divergent rapid prototyping to enable design teams and technical organizations to increase the level of transformation, speed, and value of their product/system development and design processes.If successful, the results of this research will provide insight into the underlying cognitive processes taking place as engineering designers make decisions (alone and in teams), including the relationship between the amount of stress experienced under different amounts of uncertainty based on the type of design activity and the problem solving preference of the individuals. The results will enable the development of new supportive tools and environments to assist in the resolution of complex engineering challenges, while taking the individual psychological predispositions of the engineers and the specific divergent or convergent nature of the design activity into account. This knowledge will enable the facilitation of mental pivoting between the divergent and convergent engineering design phases, as well as the maximization of each individual engineer's participation and output in both phases.