The brain organization of STEM concept knowledge: a neurally-based foundation for training, measuring, and assessing concept learning from basic knowledge to expertise

这个项目的前提是，了解STEM概念在大脑中是如何组织的，将能够加强STEM概念的学习及其评估。研究表明，尽管学生在获取与STEM概念相关的知识的准确性或完整性方面存在差异，但他们的神经归档系统非常相似，就概念知识的特定方面而言，他们的大脑系统是特定方面的场所。了解这种每个人都共有的神经组织，使我们有可能在教学过程中考虑到这种组织。实际上，它使“教给大脑”成为可能。更具体地说，它使基于现代机器学习指导的神经科学开发创新的认知训练技术成为可能，以补充传统的STEM学习。目标是制定一个详细的理论，说明多个STEM学科中的基本STEM概念知识是如何神经组织的，基础组织是如何随着学习而发展的，以及组织如何受到STEM领域中的教学和能力因素的影响。这一项目的发现将促进对科学概念本体论、教学设计理论和人工智能(AI)指导教学的理解。总而言之，这些进展将有助于更有效地干预专家一级的知识。该项目的设计将有目的地将大学和社区学院的学生纳入传统学习不足的群体中，他们具有广泛的STEM能力。该项目将评估不同专业水平的学生在4个不同领域(物理、生物、化学和数学)对STEM概念的大脑表征(使用几种功能磁共振测量)，并使用机器学习分析不同类型教学(课堂教学、实验室内概念教学和专注于专业知识的培训)下的fMRI数据来评估这些表征的变化。教学的目标将是在初学者中产生类似于讲师或领域专家的神经表征。这个项目建立在研究人员之前由NSF资助的工作的基础上，该工作证明了功能磁共振成像可以识别物理概念的潜在神经维度，并可以预测和评估这些概念的学习(比传统的行为测量更多)。这项研究继续调查神经数据如何通过将方法扩展到其他STEM领域并通过附带的神经评估指导认知指导来有效地指导教学。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

The premise of this project is that an understanding of how STEM concepts are organized in the brain would enable the enhancement of STEM concept learning and its assessment. Research has shown that although students vary in how accurately or completely they acquire knowledge related to a STEM concept, their neural filing systems are remarkably similar, in terms of which brain systems are the sites of particular aspects of concept knowledge. Understanding this neural organization common to everyone makes it possible to take that organization into account in the course of instruction. In effect, it makes it possible to “teach to the brain”. More specifically, it makes it possible to develop innovative cognitive training techniques based on modern machine-learning guided neuroscience to supplement traditional STEM learning. The goal is to formulate a detailed theory of how basic STEM concept knowledge in multiple STEM disciplines is neurally organized, how the underlying organization develops with learning, and how the organization is impacted by instructional and ability factors across STEM domains. Findings from this project would advance the understanding of the ontology of scientific concepts, theories of instructional design, and AI (machine-learning) guided instruction. Altogether these advances would facilitate more effective interventions towards expert-level knowledge. The design of this project will purposefully include both University and Community College students with a large range of STEM abilities in traditionally under-studied groups. This project will assess the brain representations of STEM concepts (using several fMRI measures) in 4 different domains (physics, biology, chemistry, and mathematics) in students at multiple levels of expertise and assess the changes in those representations using machine-learning analysis of fMRI data under different types of instruction (class instruction, in-lab concept instruction, and expertise-focused training). The goal of the instruction will be to generate neural representations in novice learners that are similar to those of instructors or domain experts. This project builds on the investigators’ prior NSF funded work which demonstrated that fMRI can identify the underlying neural dimensions of physics concepts, and can predict and assess learning of these concepts (more so than traditional behavioral measurements). This research continues investigation of how neural data can usefully guide instruction by extending the approach to additional STEM domains and by guiding cognitive instruction with the accompanying neural assessment.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.