SL-CN: Learning to Move and Moving to Learn

SL-CN：学习移动和移动学习

• 批准号: 1640909
• 负责人: Leanne Chukoskie
• 金额: $ 75万
• 依托单位: University of California-San Diego
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-09-01 至 2020-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1640909&HistoricalAwards=false
• 关键词: SL; CN; Learning; Move; Moving

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).

这个学习科学协作网络汇集了来自加州大学圣地亚哥分校和印第安纳大学的跨学科研究团队，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。识别有学习困难的个体，并为这些个体提供适当的改进机会，是当今教育面临的最大挑战之一。新的测量工具和来自运动研究的分析可以有效地解决这些问题，从而实现个性化教育的潜力。为了实现这一目标，由研究人员组成的“通过运动学习网络”（LMN）将汇集神经科学、工程学、计算和物理学方面的专业知识，研究在学校学习有困难的儿童的运动特征。在实验室之外研究运动的新工具也将被创建和测试，这些工具可以很容易地部署在教育环境中。我们期望从这项研究中获得的新知识将有助于对儿童的运动特征进行分类，因为这些特征映射到每个儿童自己的教育和认知概况。运动提供了一个了解大脑功能的重要窗口，因为大量的大脑参与运动决策、计划、执行和评估。该网络汇集了新技术和新的分析方法，以确定可以从身体，面部和眼睛的运动的细粒度测量中了解到什么。在跨职能团队中工作的LMN研究人员将分享方法和数据，以捕捉动作的细微之处，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，并潜在地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是可训练的这一事实，并旨在利用这种可塑性作为一种工具来改善认知功能，以便在未来获得更好的学习机会。此外，LMN研究者与当地公立学校和非正式科学学习地点的联系将使该小组能够参与混合研究和推广活动，强调体育活动在发展运动技能以促进认知健康方面的重要性。该奖项由SBE行为与认知科学部（BCS）和SBE多学科活动办公室（SMA）资助，由学习-协作网络科学（SL-CN）计划授予。

项目成果

Distraction “Hangover”: Characterization of the Delayed Return to Baseline Driving Risk After Distracting Behaviors

分心 — 宿醉 —：分心行为后延迟返回基线驾驶风险的特征

• DOI: 10.1177/00187208211012218
• 发表时间: 2021
• 期刊: Human Factors: The Journal of the Human Factors and Ergonomics Society
• 作者: Snider, Joseph;Spence, Ryan J.;Engler, Anne-Marie;Moran, Ryan;Hacker, Sarah;Chukoskie, Leanne;Townsend, Jeanne;Hill, Linda
• 通讯作者: Hill, Linda

An Objective System for Quantifying the Effect of Cognitive Load on Movement in Individuals with Autism Spectrum Disorder

量化认知负荷对自闭症谱系障碍患者运动影响的客观系统

• DOI: 10.1109/ner.2019.8717022
• 发表时间: 2019
• 期刊: IEEE/EMBS Conference on Neural Engineering
• 作者: Simmons, Trent L.;Snider, Joseph;Amit, Moran;Ng, Tse Nga;Townsend, Jeanne;Chukoskie, Leanne
• 通讯作者: Chukoskie, Leanne

Quantifying Gaze Behavior During Real-World Interactions Using Automated Object, Face, and Fixation Detection

• DOI: 10.1109/tcds.2018.2821566
• 发表时间: 2018-03
• 期刊: IEEE Transactions on Cognitive and Developmental Systems
• 影响因子: 5
• 作者: L. Chukoskie;Shengyao Guo;Eric Ho;Yalun Zheng;Qiming Chen;Vivian Meng;John Cao;Nikhita Devgan;Si Wu;P. Cosman