Seeing Scientifically: Scaffolding Observation of Complex Visual Phenomena

科学观察项目将研究一种新的方式来支持博物馆参观者的体验，这样他们就可以对活的显微镜标本进行真实的科学观察。通过采用当前生物学研究中现有的计算成像技术，该项目旨在鼓励和支持参观者进行科学观察，即提出富有成效的问题，解释丰富的图像信息，并从日益成为当前生物学研究特征的视觉证据中做出推断。脚手架(例如，视觉提示或支持学习的信息)将由一个虚拟指南和提示系统组成，该系统对访问者所看到的做出反应。脚手架提示将覆盖在实时、高密度的活样本图像上，参观者正在用研究级显微镜观察该样本。项目研究将提供关于如何利用科学家自己使用的设备和技术看到的复杂、动态的视觉证据，为非正式学习者在真实的科学观察实践中搭建基础的早期知识。项目研究和资源将通过出版物、会议演讲和通过NSF支持的informalScience网站和其他相关网站共享资源，广泛传播给学习科学研究人员、非正式科学从业人员和其他感兴趣的受众。作为其加强非正式环境中学习的总体战略的一部分，推进非正式STEM学习(AISL)计划为各种环境下使用的创新资源提供资金。这包括为扩大获得和参与STEM学习经验提供多种途径，推进对非正式环境中STEM学习的创新研究和评估，以及加深参与者对更深层次学习的理解。该项目将制作一个创新的显微镜展览的原型，为参观者提供对活标本及其生物过程的科学观察。最重要的假设是，对实时视觉现象的科学观察可以让学习者深度参与现代科学的内容、工具和实践，现代科学越来越依赖于基于图像的数据。通过三轮迭代原型开发和评估，该项目将为以下相关问题产生早期发现：(1)在非中介展览上观察活标本的有前景的方法是什么；(2)如何将计算成像技术整合到显微镜展览中，以吸引和支撑学习者提出富有成效的问题，解释他们看到的，并从复杂的、动态的图像中做出基于证据的推理。将通过以下方式收集和分析数据：对参观者进行有声思考采访，了解他们对观察到的生物现象的兴趣和描述；对参观者提问类型和答案、相关特征、推论和脚手架使用情况的有声思考记录进行编码；以及对停留时间、提问、回答、推论和脚手架使用进行统计比较。项目成果将为脚手架和计算成像技术的组合提供更严格的研究，有效地支持图像丰富的科学领域的科学观察。

The Seeing Scientifically project will research a new way of supporting museum visitor experiences so they can have authentic scientific observation of live microscopic specimens. By adapting existing computational imaging techniques from current biological research, the project aims to encourage and support visitors in observing scientifically, that is, in asking productive questions, interpreting image-rich information, and making inferences from visual evidence that increasingly characterizes current biological research. The scaffolding (e.g., visual cues or information supporting learning) will consist of a system of virtual guides and prompts that are responsive to what visitors see. The scaffolding prompts will be overlaid on a real time, high-density image of a live sample that the visitor is investigating with a research grade microscope. Project research will contribute early knowledge on ways to scaffold informal learners in the practice of authentic scientific observation with the complex, dynamic visual evidence that scientists themselves see using the equipment and techniques they use. Project research and resources will be widely disseminated to learning science researchers, informal science practitioners, and other interested audiences through publications, conference presentations and sharing of resources via the NSF-supported informalscience.org website and other relevant websites.As part of its overall strategy to enhance learning in informal environments, the Advancing Informal STEM Learning (AISL) program funds innovative resources for use in a variety of settings. This includes providing multiple pathways for broadening access to and engagement in STEM learning experiences, advancing innovative research on and assessment of STEM learning in informal environments, and developing understandings of deeper learning by participants. The project will prototype an innovative microscope exhibit that scaffolds visitors in scientific observation of live specimens and their biological processes. The overarching hypothesis is that scientific observation of real-time visual phenomena can deeply engage learners with the content, tools and practices of modern science, which increasingly rely on image-based data. Through three rounds of iterative prototype development and evaluation, the project will generate early findings for the following related questions: (1) what are promising ways of scaffolding observation of live specimens at an unmediated exhibit; (2) How can computational imaging techniques be integrated into a microscope exhibit to engage and scaffold learners to ask productive questions, interpret what they see, and make evidence-based inferences from complex, dynamic images. Data will be collected and analyzed by coding think-aloud interviews with visitors concerning their interest in and description of the biological phenomenon observed; coding of think aloud transcripts of visitor questions types and answers, relevant features noted, inferences and scaffold use; and statistical comparison of holding time, questions asked, answers, inferences, and scaffold use. Project findings will seed more rigorous research on the combination of scaffolding and computational imaging techniques effective for supporting scientific observation in image-rich areas of science.