RAPID: Ethnographic field study of engineering knowledge systems: Developing a social-process-centric situated model

这项由俄勒冈州立大学实施的项目将通过研究NASA的一组研究人员，在他们开发生物哨兵纳米卫星的设计时，推动以社会过程为中心的工程知识系统情境模型的开发。这颗卫星将测量太空中辐射对生物的影响，并将成为第一个在太空中远程操作的生物传感器。BioSentinel项目的复杂性和史无前例的性质为研究工程师团队如何在高度受限的纳米卫星组件的尺寸、重量和功率容量中协商相互冲突和相互竞争的需求提供了一个独特的机会。了解工程师从业者的知识体系以及他们如何跨越差异进行沟通，对于开发更贴近实际工程实践的工程课程至关重要。Bio Sentinel项目将汇集具有基因工程、光学、微流体、控制系统、通信、太阳能和电池电力、推进、制导和导航系统、辐射物理、机器人、软件和用户界面以及热调节方面的专业知识的个人。这组工程师将集中在一个地点集中工作一段时间，赶在严格的最后期限前工作。Bio Sentinel设计提供了一个独特的机会，可以研究工程专业知识是如何实际应用的，并记录具有相互竞争知识的工程师通过这些社会过程进行交互、达成共识和创建复杂的技术系统。了解工程师跨越差异进行有效沟通的过程有助于将工程知识重新构建为一种社会、情景实践，并将有助于创建与创建复杂技术系统的工作场所需求相关的工程教育课程。俄勒冈州立大学团队将把这项实地研究构建为认识论人种学。这将是一种身临其境的参与式观察方案，旨在捕捉一个群体知识实践的可靠和有效证据。该项目设计认识到，认知科学和学习科学最近的一个重大转变是从以个人为中心的认知认知模型转向以社会过程为中心的情景模型。有证据表明，语境，尤其是经常被故意从书房、教室和教科书中移除的社会、人文语境，在一个人可以说是知道的东西中扮演着至关重要的角色。情景模式将重点从学生的知识和技能转移到他们在特定社会制度中的行为上。为了理解STEM学生需要知道的方式和内容，必须从社会环境中获取数据，以了解在工作环境中应用这些知识的过程。对BioSentinel设计的研究将把前沿的学习和认知理论与工程知识和社会科学研究方法结合起来，同时应用和发展对工程知识系统的理解，如应用于异常复杂和开放的技术系统设计中。

This project conducted by Oregon State University will advance the development of a social-process-centric situated model of engineering knowledge systems by a studying a group of NASA researchers as they develop the design of the BioSentinel nano satellite. This satellite will measure the effects of radiation in space on living organisms and will be the first biosensor operated remotely in space. The complexity and unprecedented nature of the BioSentinel project provides a unique opportunity to study how a team of engineers negotiates conflicting and competing demands in the highly restricted size, weight, and power capacity of a nano satellite package. Understanding the knowledge systems of engineer practitioners and how they communicate across differences is crucial to the development of an engineering curriculum that aligns more closely to actual engineering practice. The Bio Sentinel project will bring together individuals with expertise in genetic engineering, optics, microfluidics, control systems, communications, solar and battery power, propulsion, guidance and navigation systems, radiation physics, robotics, software and user interface, and thermal regulation. This group of engineers will be convened together for concentrated period of time in a single location working against strict deadlines. The Bio Sentinel design offers a unique opportunity to study how engineering expertise is actually applied and to document the social processes by which engineers with competing knowledge claims interact, reach consensus, and create a complex technological system. Understanding the processes by which engineers effectively communicate across differences helps to reframe engineering knowledge as a social, situated practice and will help to create an engineering education curriculum that is relevant to the workplace demands of creating complex technological systems.The Oregon State team will structure this field study as an epistemological ethnography. This will be an immersive, participatory observation protocol designed to capture reliable and valid evidence of a group's knowledge practices. The project design recognizes that a significant recent shift in cognitive and learning sciences has been the move from individual-centric cognitive models of cognition to social-process-centric situative models. There is evidence that context and particularly, exactly the social, human context that is so often intentionally removed from studies, classrooms, and textbooks plays a vitally important role in what a person can be said to know. Situative models shift the focus from the students' knowledge and skills to their actions in specific social systems. To understand how and what STEM students need to know, data must be derived from social the processes surrounding the application of this knowledge in workplace environments. Study of the BioSentinel design will combine leading-edge theories of learning and cognition with engineering knowledge and social science research methods to simultaneously apply and develop an understanding of engineering knowledge systems as applied in an exceptionally complex and open-ended technological system design.