Seeing Scientifically: Scaffolding Observation of Complex Visual Phenomena

Seeing Scientists项目将研究一种支持博物馆游客体验的新方法，以便他们能够对活的显微标本进行真实的科学观察。通过调整现有的计算成像技术，从目前的生物研究，该项目旨在鼓励和支持游客在科学观察，即在提出富有成效的问题，解释图像丰富的信息，并作出推论，从视觉证据，越来越多的特点，目前的生物研究。 脚手架（例如，视觉提示或支持学习的信息）将包括一个虚拟导游和提示系统，对游客所看到的内容作出反应。 脚手架提示将覆盖在一个活的样品的真实的时间，高密度的图像，游客正在研究级显微镜。 项目研究将提供早期知识，帮助非正式学习者在真实科学观察的实践中使用科学家自己使用设备和技术看到的复杂，动态的视觉证据。 项目研究和资源将通过出版物、会议介绍和通过国家科学基金会支持的www.example.com网站和其他相关网站共享资源，向学习科学研究人员、非正式科学工作者和其他感兴趣的受众广泛传播informalscience.org。推进非正式STEM学习（AISL）计划为各种环境中使用的创新资源提供资金。这包括提供多种途径，以扩大获得和参与STEM学习经验，推进非正式环境中STEM学习的创新研究和评估，以及发展参与者对深入学习的理解。该项目将建立一个创新的显微镜展览原型，让参观者对活标本及其生物过程进行科学观察。总体假设是，对实时视觉现象的科学观察可以使学习者深入了解现代科学的内容、工具和实践，而现代科学越来越依赖于基于图像的数据。 通过三轮迭代的原型开发和评估，该项目将为以下相关问题产生早期发现：（1）在无中介展览中有哪些有前途的支架观察活体标本的方法;（2）如何将计算成像技术集成到显微镜展览中，以吸引和支持学习者提出富有成效的问题，解释他们所看到的，并从复杂的动态图像中做出基于证据的推断。将通过对来访者进行关于其对所观察到的生物现象的兴趣和描述的有声思维访谈进行编码来收集和分析数据;对来访者问题类型和答案的有声思维转录本进行编码，注意到相关特征，推断和支架使用;以及对保持时间，提问，答案，推断和支架使用进行统计比较。项目的研究结果将为脚手架和计算成像技术的结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.