SL-CN: Learning to Move and Moving to Learn

点击翻译按钮获取中文摘要

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).