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.

该项目解决了围绕贯穿科学学科的“大思想”更有效地组织STEM（科学、技术、工程和数学）教学的迫切需要。包括国家研究理事会和大学理事会在内的政策领导人有力地推动了这一需求。他们指出，当学生在一个一致的知识框架内组织和链接信息时，学习会更有效，这是大思想应该提供的。不幸的是，找到有效地教授大思想的方法--使它们成为有用的知识框架--是一个重大的挑战。深层结构建模（DSM），在这个项目中先进的创新，旨在满足高中生物学的背景下，这一挑战。在DSM中，学生学习一个大的想法作为一组包含结构的例子的底层或“深层”结构，但具有不同的外部细节。随着学习者开始理解深层结构（即，根据支持DSM的学习理论，这一过程的结果应该是一个灵活的、有意义的、易于表达的大概念，从而为理解学习者遇到的新信息提供了一个理想的框架（即，学习大思想（Learn with the Big idea）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，它为如何围绕大思想组织教学和学习提供了重要的知识，不仅用于深层结构建模，还用于其他教学方法。该项目有两个研究和原型开发部分。这两个项目都是在高中生物学的背景下进行的，在三个地区的九个教室里进行，支持多达610名学生。这项工作的重点是DSM的三个设计特点：（1）嵌入模型源材料与直观的，机械的想法;（2）支持学习者抽象这些想法作为一组源共享的深层结构;（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个抽象的，直观丰富的，高效的知识结构，他们随后使用作为一个框架来解释，组织和链接学科内容。一系列的五项研究相互建立，以了解设计特征是否以及如何带来这些预期的效果。在序列中的早期研究是小规模的课堂实验，随机分配学生的深度结构建模或平行，非建模控制。措施歧视的预期效果在学习和后测。后来的研究使用定性方法仔细跟踪随着时间的推移和跨主题的预期效果。作为一个群体，这些研究正在为学习者如何有效地抽象和表达大思想以及如何利用这些思想作为理解学习内容的框架提供一般性知识。随着研究的发展，两个经过研究测试的生物课程原型正在开发中：一个以能源为中心的四分之一年DSM生物课程;和一个以自然选择为中心的八年DSM单元。探索研究preK-12计划（DRK-12）旨在通过研究和开发新的创新和方法，显着提高preK-12学生和教师的科学，技术，工程和数学的学习和教学。DRK-12项目中的项目建立在STEM教育的基础研究以及为项目提供理论和经验依据的先前研究和开发工作的基础上。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。