Combining Smartphone Light Detection and Ranging with Augmented Reality to Enhance Position-Based Teaching and Learning in STEM

了解如何测量，显示和解释运动对于许多STEM相关的职业非常重要，特别是在物理和数据科学领域。教育研究人员提倡许多方法来支持运动的数学模型的意义，但教师往往很难制定他们由于资源有限。该项目将通过构建基于光的移动的传感器（LiDAR），使任何拥有智能手机的人都能实现高精度的位置感测，这些传感器能够检测一个人与物体的距离和空间内的位置。教育研究将衡量使用这种新技术来改善学生学习和参与数学模型与运动图的效果，方法是制作一个课堂就绪的应用程序和游戏化课程，供教师和学生在传统教室和家庭中使用。研究人员和教育软件开发人员将基于iOS的扫描LiDAR和Android的飞行时间深度成像开发新的数据可视化技术。拟议的技术创新将利用新型背对红外光束阵列，根据用户的移动和环境数据，显着提高位置测量的精度和增强现实（AR）可视化的位置。该项目将确定激光雷达辅助AR技术在多大程度上能够实现高精度、基于位置的实时数据可视化。它将探讨新技术如何提供一种认知支架和具体的经验，需要推进教学与图形和矢量建模运动。学习科学的研究将需要与STEM教育工作者合作，开发和测试传统和远程学习环境中探索场景的有效性。该奖项将评估全身运动，以理解运动图，重点是具体的学习和实践与数据可视化素养。该奖项反映了NSF的法定使命，并已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。

Understanding how to measure, display, and interpret motion is important for many STEM-related careers, particularly in the physical and data sciences. Educational researchers have advocated for numerous approaches to support sense-making with mathematical models of motion, but teachers often struggle to enact them due to limited resources. This project will make high-precision position sensing a reality for anyone who owns a smartphone by building on light-based mobile sensors (LiDAR) that are able to detect one’s distance from objects and location within a space. The educational research will measure the effect of using this new technology to improve student learning and engagement with regard to mathematical models with motion graphs, by producing a classroom-ready application and gamified lessons for teachers and students to use in traditional classrooms as well as the home. Researchers and educational software developers will develop new data visualization technology based on iOS’ scanning LiDAR and Android’s time-of-flight depth imaging. The proposed technological innovation will make use of the novel back-facing infrared beam array to significantly increase precision in position measurements and the placement of augmented reality (AR) visualizations based on users’ movements and environmental data. This project will determine the extent to which LiDAR-aided AR technology can enable high-precision, position-based, and real-time data visualization. It will explore how the new technology can provide the kind of cognitive scaffolding and embodied experiences needed for advancing teaching about modeling motion with graphs and vectors. Research in the learning sciences will entail a collaboration with STEM educators to develop and test the effectiveness of scenarios for exploration in traditional and remote learning contexts. This proposal will assess full-body movement to make sense of motion graphs with a focus on embodied learning and practice with data visualization literacy.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.