Collaborative Research: How Deep Structural Modeling Supports Learning with Big Ideas in Biology

协作研究：深度结构建模如何支持生物学大思想的学习

• 批准号: 2010223
• 负责人: Daniel Capps
• 金额: $ 54.97万
• 依托单位: University of Georgia Research Foundation Inc
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-08-01 至 2025-07-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2010223&HistoricalAwards=false
• 关键词: Collaborative; Research; How; Deep; Structural

This project addresses the pressing need to more effectively organize STEM (science, technology, engineering, and mathematics) teaching and learning around “big ideas” that run through science disciplines. This need is forcefully advanced by policy leaders including the National Research Council and the College Board. They point out that learning is more effective when students organize and link information within a consistent knowledge framework, which is what big ideas should provide. Unfortunately, finding ways to teach big ideas effectively—so they become useful as knowledge frameworks— is a significant challenge. Deep structure modeling (DSM), the innovation advanced in this project, is designed to meet this challenge in the context of high school biology. In DSM, students learn a big idea as the underlying, or “deep” structure of a set of examples that contain the structure, but with varying outward details. As learners begin to apprehend the deep structure (i.e., the big idea) within the examples, they use the tools and procedures of scientific modeling to express and develop it. According to theories of learning that undergird DSM, the result of this process should be a big idea that is flexible, meaningful, and easy to express, thus providing an ideal framework for making sense of new information learners encounter (i.e., learning with the big idea). To the extent that this explanation is born out in rigorous research tests and within authentic curriculum materials, it contributes important knowledge about how teaching and learning can be organized around big ideas, and not only for deep structural modeling but for other instructional approaches as well. This project has twin research and prototype development components. Both are taking place in the context of high school biology, in nine classrooms across three districts, supporting up to 610 students. The work focuses on three design features of DSM: (1) embedding model source materials with intuitive, mechanistic ideas; (2) supporting learners to abstract those ideas as a deep structure shared by a set of sources; and (3) representing this deep structure efficiently within the model. In combination, these features support students to understand an abstract, intuitively rich, and efficient knowledge structure that they subsequently use as a framework to interpret, organize, and link disciplinary content. A series of five research studies build on one another to develop knowledge about whether and how the design features bring about these anticipated effects. Earlier studies in the sequence are small-scale classroom experiments randomly assigning students to either deep structural modeling or to parallel, non modeling controls. Measures discriminate for the anticipated effects during learning and on posttests. Later studies use qualitative methods to carefully trace the anticipated effects over time and across topics. As a group, these studies are contributing generalized knowledge of how learners can effectively abstract and represent big ideas and how these ideas can be leveraged as frameworks for learning content with understanding. Two research-tested biology curriculum prototypes are being developed as the studies evolve: a quarter-year DSM biology curriculum centered on energy; and an eighth-year DSM unit centered on natural selection. The Discovery Research preK-12 program (DRK-12) seeks to significantly enhance the learning and teaching of science, technology, engineering and mathematics by preK-12 students and teachers, through the research and development of new innovations and approaches. Projects in the DRK-12 program build on fundamental research in STEM education and prior research and development efforts that provide theoretical and empirical justification for the projects.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

该项目应对更有效地组织STEM（科学，技术，工程和数学）的迫切需求，围绕科学学科运行的“大思想”进行教学。包括国家研究委员会和大学委员会在内的政策领导人有力地提出了这一需求。他们指出，当学生在一致的知识框架内组织和链接信息时，学习更加有效，这是大创意应该提供的。不幸的是，寻找有效教导大思想的方法 - 因此它们作为知识框架有用 - 是一个重大挑战。深层结构建模（DSM）是该项目中提出的创新，旨在在高中生物学的背景下应对这一挑战。在DSM中，学生学习了一个大想法，即包含结构但外在细节的一组示例的基础或“深”结构。当学习者开始在示例中逮捕深层结构（即大思想）时，他们使用科学建模的工具和程序来表达和开发它。根据学习基金会DSM的理论，此过程的结果应该是一个很大的想法，它是灵活，有意义且易于表达的，因此为了解新信息学习者遇到的理想框架（即学习大想法）提供了理想的框架。在某种程度上，这种解释是在严格的研究测试和真实的课程材料中诞生的，它有助于有关如何围绕大思想组织教学和学习的重要知识，而不仅是针对深层的结构建模，而且对于其他教学方法。该项目具有双研究和原型开发组件。两者都在高中生物学的背景下，在三个地区的九个教室中进行，最多支持610名学生。该作品重点介绍了DSM的三个设计特征：（1）将模型源材料嵌入具有直观的机械思想； （2）支持学习者将这些想法抽象为一组资源共享的深层结构； （3）在模型中有效地表示这种深层结构。结合起来，这些功能支持学生理解一种抽象，丰富和有效的知识结构，后来他们用作解释，组织和链接学科内容的框架。一系列五项研究相互依靠，以发展有关设计特征是否以及如何带来这些预期影响的知识。序列中的早期研究是小规模的课堂实验，将学生随机分配给了深层结构建模或平行的非建模控制。测量在学习和测试期间对预期的影响区分。后来的研究使用定性方法来仔细地追踪随时间和跨主题的预期效果。作为一个小组，这些研究正在为学习者如何有效地抽象和代表大思想以及如何利用这些思想作为学习内容的框架，以了解与理解的框架。随着研究的发展，正在开发两个研究测试的生物学课程原型：以能源为中心的四分之一DSM生物学课程；以及以自然选择为中心的八年级DSM单位。 Discovery Research Prek-12计划（DRK-12）试图通过新的创新和方法的研究和开发来大大增强PreK-12学生和教师对科学，技术，工程和数学的学习和教学。 DRK-12计划中的项目基于STEM教育和先前的研发工作的基础研究，为项目提供了理论和经验的理由。该奖项反映了NSF的法定任务，并通过使用基金会的知识分子的优点和更广泛的影响来评估NSF的法定任务。