An Instructional Complexity Approach to the Science of Learning by Analogy

类比学习科学的教学复杂性方法

• 批准号: 1548292
• 负责人: Lindsey Richland
• 金额: $ 30万
• 依托单位: University of Chicago
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-09-01 至 2018-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1548292&HistoricalAwards=false
• 关键词: Instructional; Complexity; Approach; Science; Learning

Mathematics teaching that is flexible, transferrable, and connected across topics is crucial to high quality instruction. This exploratory project addresses this goal by a potentially transformational approach that leverages and extends science of learning research on analogy, a promising mode for supporting flexible and connected mathematical thinking. This study proposes an approach different from much of lab-based research designed to focus on isolating specific mechanisms of learning for study. Instead, the researchers will intentionally examine multiple learning processes combinatorially and as situated in classrooms. Many mechanisms of learning operate simultaneously in naturalistic classroom situations, and may either augment or undermine each other. Therefore, more theories that explain how students learn from the interaction of cognitive principles/processes is urgently needed. Research that is better aligned with learning in classroom settings will also make research findings more applicable for use, enabling teachers to more easily adopt research-based recommendations for classroom practice. The investigators will test this approach by leveraging a set of "translation ready" principles" for spatially supporting analogy during STEM learning. These are principles that produce high learning outcomes and for which there is classroom evidence for their usability and efficacy. As these principles are implemented in naturalistic classroom settings, this study takes seriously the diverse ways in which they may be realistically enacted (or not) in the classroom, in different combinations and sequences that may change the expected outcomes. The principles for supporting mathematics classroom analogy in this study were developed by the NSF supported Spatial Intelligence and Learning Center (SILC), and they include: simultaneous visual representations, spatial alignment, linking gesture, and sensitivity to children's cognitive resources. Study 1 is a new analysis of existing data to compare how teachers in the U.S. versus two higher achieving regions: Hong Kong and Japan, use and combine these principles when teaching 8th grade mathematics by analogy. Study 2 will be deep interviews with a set of expert teachers for dissemination of these principles and the results about their combinatorial frequencies. Teacher feedback on these alternatives will be elicited, as well as their input regarding best practices of analogy for their own students. Study 3 will experimentally compare three versions of a video-based lesson - one involving multiple support strategies as modal in the high achieving regions, one involving the modal U.S. combination of supports or violations, and one with the optimal combination predicted by SILC. WM and EF measures will also be administered to explore whether combined supports increase or reduce processing load. Together, these studies will provide rich data on the way spatial analogy principles are 1) typically combined in practice, and 2) optimally integrated to support students' learning in mathematics classrooms. This data will support the generation of theory that addresses how cognitive mechanisms interact to support students' mathematical thinking.Broader Impacts: The project has the potential for broad impact on mathematics teaching as well as on research in the science of learning. The project takes an innovative approach to shifting the focus of theory driven research to incorporate complexity without losing rigor, making learning theory better able to explain and inform teaching practice within the complexity of everyday classrooms. A broad dissemination plan for best practices of analogy instruction is in place.

数学教学是灵活的，可转移的，跨主题的连接是高质量教学的关键。 这个探索性的项目通过一种潜在的变革性方法来实现这一目标，这种方法利用并扩展了类比学习研究的科学，这是一种支持灵活和连接数学思维的有前途的模式。 这项研究提出了一种不同于许多基于实验室的研究的方法，旨在专注于隔离学习的特定机制。相反，研究人员将有意识地研究多个学习过程的组合和位于教室。 许多学习机制在自然主义的课堂情境中同时运作，它们可能相互增强，也可能相互削弱。因此，迫切需要更多的理论来解释学生如何从认知原则/过程的相互作用中学习。 更好地与课堂环境中的学习相结合的研究也将使研究结果更适用于使用，使教师能够更容易地将基于研究的建议用于课堂实践。研究人员将通过利用一套“翻译就绪”原则来测试这种方法，以便在STEM学习期间在空间上支持类比。这些原则产生高的学习成果，并有课堂证据证明其可用性和有效性。由于这些原则是在自然主义的课堂环境中实施的，本研究认真考虑了不同的方式，它们可能会在课堂上现实地制定（或不），在不同的组合和序列，可能会改变预期的结果。本研究中支持数学课堂类比的原则是由美国国家科学基金会支持的空间智力和学习中心（SILC）开发的，它们包括：同时视觉表征，空间对齐，连接手势和对儿童认知资源的敏感性。研究1是对现有数据的一项新分析，旨在比较美国教师与两个成绩较高的地区（香港和日本）在类比法教授8年级数学时如何使用和联合收割机结合这些原则。研究2将深入访谈一组专家教师传播这些原则和他们的组合频率的结果。教师对这些替代方案的反馈将被引出，以及他们对自己的学生的类比的最佳做法的投入。研究3将实验比较三个版本的视频为基础的教训-一个涉及多个支持策略的模式，在高成就地区，一个涉及模态美国的支持或违反的组合，一个与SILC预测的最佳组合。工作记忆和执行功能的措施也将管理，以探讨是否联合支持增加或减少处理负荷。总之，这些研究将提供丰富的数据的方式空间类比原则1）通常结合在实践中，2）最佳集成，以支持学生在数学课堂上的学习。这些数据将支持理论的产生，解决认知机制如何相互作用，以支持学生的数学思维。更广泛的影响：该项目有可能对数学教学和学习科学的研究产生广泛的影响。该项目采用创新的方法，将理论驱动研究的重点转移到复杂性而不失去严谨性，使学习理论能够更好地解释和告知日常课堂复杂性中的教学实践。已经制定了广泛传播类比教学最佳做法的计划。