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

该项目的前提是，了解STEM概念在大脑中的组织方式将有助于加强STEM概念学习及其评估。研究表明，尽管学生在获得与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，就大脑系统是概念知识的特定方面而言。了解这种每个人都共有的神经组织，就有可能在教学过程中考虑到这种组织。实际上，它使“向大脑教学”成为可能。更具体地说，它使开发基于现代机器学习指导神经科学的创新认知训练技术成为可能，以补充传统的STEM学习。我们的目标是制定一个详细的理论，如何在多个STEM学科的基本STEM概念知识是神经组织，底层组织如何发展与学习，以及组织是如何影响跨STEM领域的教学和能力因素。该项目的研究结果将促进对科学概念本体论、教学设计理论和人工智能（机器学习）指导教学的理解。 总之，这些进展将有助于更有效地干预专家级知识。该项目的设计将有目的地包括大学和社区学院的学生在传统上未充分研究的群体中具有广泛的STEM能力。该项目将在4个不同领域（物理，生物，化学和数学）评估STEM概念的大脑表征（使用几种功能磁共振成像测量）在多个专业水平的学生中，并使用不同类型的指令（课堂教学，实验室概念教学和专业培训）下的功能磁共振成像数据的机器学习分析来评估这些表征的变化。该指令的目标是在新手学习者中生成类似于教师或领域专家的神经表征。 该项目建立在研究人员之前NSF资助的工作基础上，该工作表明fMRI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好）。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领域并通过伴随的神经评估指导认知教学来有效地指导教学。该奖项反映了NSF的法定使命，并被认为值得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来支持。

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.