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）教学和学习的迫切需要。包括国家研究委员会和大学理事会在内的政策领导人大力推动了这一需求。他们指出，当学生在一致的知识框架内组织和链接信息时，学习会更加有效，而这正是伟大思想应该提供的。不幸的是，找到有效教授重要思想的方法（使它们成为有用的知识框架）是一项重大挑战。深层结构建模 (DSM) 是该项目中先进的创新，旨在应对高中生物背景下的这一挑战。在 DSM 中，学生学习一个大思想，作为一组示例的基础或“深层”结构，这些示例包含该结构，但具有不同的外部细节。当学习者开始理解示例中的深层结构（即大思想）时，他们使用科学建模的工具和程序来表达和发展它。根据支撑 DSM 的学习理论，这个过程的结果应该是一个灵活的、有意义的、易于表达的大思想，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，用大思想来学习）。从某种程度上来说，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，它提供了关于如何围绕大思想组织教学和学习的重要知识，不仅适用于深层结构建模，也适用于其他教学方法。该项目具有研究和原型开发两个部分。这两场活动均以高中生物为背景，在三个学区的 9 个教室中进行，最多可容纳 610 名学生。该工作重点关注DSM的三个设计特点：（1）以直观、机械的思想嵌入模型源材料； (2) 支持学习者将这些想法抽象为由一组来源共享的深层结构； (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抽象、直观、丰富和高效的知识结构，随后他们将其用作解释、组织和链接学科内容的框架。一系列五项研究相互基础，以了解设计特征是否以及如何带来这些预期效果。该序列中的早期研究是小规模的课堂实验，将学生随机分配给深度结构建模或并行的非建模控制。措施对学习期间和后测试的预期效果有所区别。后来的研究使用定性方法来仔细追踪随着时间的推移和跨主题的预期效果。作为一个整体，这些研究正在贡献关于学习者如何有效抽象和表达重要思想以及如何利用这些思想作为理解学习内容的框架的一般知识。随着研究的进展，两个经过研究测试的生物课程原型正在开发中：以能源为中心的每季度 DSM 生物课程；以及以自然选择为中心的第八年 DSM 单元。发现研究 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 计划中的项目建立在 STEM 教育的基础研究和先前的研究和开发工作的基础上，为这些项目提供理论和实证依据。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。