SL-CN: Learning to Move and Moving to Learn

这个学习科学合作网络汇集了来自加州大学圣地亚哥分校和印第安纳州大学的跨学科研究人员团队，研究儿童的运动如何影响他们的学习以及学习如何影响运动技能。识别有学习困难的人，并为这些人提供适当的改进机会，是当今教育面临的最大挑战之一。新的测量工具和运动研究的分析可以有效地解决这些问题，以实现个性化教育的潜力。为了实现这一目标，通过运动学习网络（LMN）的研究人员将汇集来自神经科学，工程，计算和物理学的专业知识，研究在学校遇到学习困难的儿童的运动特征。还将创建和测试用于实验室外运动研究的新工具，这些工具可以随时部署在教育环境中。预计从这项研究中产生的新知识将有助于对儿童的运动签名进行分类，因为这些签名映射到每个儿童自己的教育和认知概况。运动为大脑功能提供了一个重要的窗口，因为大量的大脑参与了运动决策，计划，执行和评估。该网络汇集了新技术和新颖的分析方法，以确定可以从身体，面部和眼睛的精细测量中学到什么。在跨职能团队中工作的LMN研究人员将分享方法和数据，以捕捉运动的微妙之处，并在任何时间点为学习者创建签名。网络中的三个核心项目汇集了来自不同领域的理论和分析方法，以识别看似不同的学习问题中基于运动的共性，并可能识别看似相似的学习问题中基于运动的差异。LMN网络将利用运动系统非常可训练的事实，并旨在利用这种可塑性作为改善认知功能的工具，以便在未来获得更好的学习机会。此外，LMN调查员与当地公立学校和非正式科学学习网站的联系将使该小组能够参与混合研究和外展活动，突出体育活动在认知健康运动技能发展中的重要性。该奖项来自学习科学合作网络（SL-CN）计划，由行为和认知科学（BCS）的SBE部门和多学科活动（SMA）的SBE办公室资助。

This Science of Learning Collaborative Network brings together an interdisciplinary team of researchers from University of California-San Diego and Indiana University, to study how children's movements impact their learning and how learning impacts movement skill. Identifying individuals with learning difficulties and matching these individuals with appropriate opportunities for improvement is one of the greatest challenges faced in education today. Novel measurement tools and analyses from the study of movement can be productively brought to bear on these problems to realize the potential of personalized education. To achieve this goal, the Learning through Movement Network (LMN) of investigators will bring together expertise from neuroscience, engineering, computation and physics, to study movement signatures in children who experience learning difficulties in school. New tools for the study of movement outside of the lab, and that can be readily deployed in educational settings, will also be created and tested. It is expected that the new knowledge created from this research will be useful in classifying children's movement signatures as those signatures map onto each child's own educational and cognitive profile. Movement offers an important window into brain function as a remarkable amount of the brain is engaged in movement decisions, planning, execution and evaluation. This network brings together new technology and novel analytic methods to determine what can be learned from fine-grained measurement of movement of the body, the face, and the eyes. LMN investigators working in cross-functional teams will share methods and data to capture the subtleties of movement and create a signature for a learner at any point in time. The three core projects in the network bring together theories and analytical methods from different fields to identify movement-based commonalities across seemingly different learning problems and potentially also identify movement-based differences across seemingly similar learning problems. The LMN network will leverage the fact that the motor system is quite trainable, and aims to use this plasticity as a tool to improve cognitive functioning for better learning opportunities in the future. In addition, LMN investigator links to local public schools and sites for informal science learning will allow the group to engage in blended research and outreach events highlighting the importance of physical activity in the development of motor skill for cognitive fitness.The award is from the Science of Learning-Collaborative Networks (SL-CN) Program, with funding from the SBE Division of Behavioral and Cognitive Sciences (BCS) and the SBE Office of Multidisciplinary Activities (SMA).