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相关的职业来说非常重要，特别是在物理和数据科学领域。教育研究人员已经提出了许多方法来支持用运动的数学模型来建立意义，但由于资源有限，教师往往难以实施这些方法。该项目将通过基于光的移动传感器（LiDAR），能够探测到一个人与物体的距离和空间内的位置，使拥有智能手机的任何人都能实现高精度的位置感知。这项教育研究将测量使用这项新技术的效果，通过制作一个课堂就绪的应用程序和游戏化的课程，让教师和学生在传统教室和家庭中使用，来提高学生在运动图形数学模型方面的学习和参与。研究人员和教育软件开发商将基于iOS的扫描激光雷达和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向红外光束阵列来显着提高位置测量的精度，并根据用户的运动和环境数据放置增强现实（AR）可视化。该项目将确定激光雷达辅助增强现实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脚手架和具体的经验，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情景的有效性。本提案将评估全身运动，使运动图形具有意义，重点是体现学习和实践数据可视化素养。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.