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与STEM相关的职业非常重要，尤其是在物理和数据科学领域。教育研究人员倡导用数学运动模型来支持意义形成的多种方法，但由于资源有限，教师往往难以实施这些方法。该项目将通过构建基于光的移动传感器(LiDAR)，使任何拥有智能手机的人都能实现高精度的位置感知，这种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项新技术改善学生对带有运动图表的数学模型的学习和参与的效果，方法是制作可在课堂上使用的应用程序和供教师和学生在传统课堂和家庭中使用的游戏化课程。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。这项拟议的技术创新将利用新型后向红外光束阵列，显著提高基于用户移动和环境数据的位置测量和增强现实(AR)可视化放置的精度。该项目将确定激光雷达辅助增强现实技术能够在多大程度上实现高精度、基于位置的实时数据可视化。它将探索这项新技术如何提供推进使用图形和矢量建模运动的教学所需的认知脚手架和具体体验。在学习科学方面的研究将需要与STEM教育工作者合作，以开发和测试在传统和远程学习环境中探索的情景的有效性。这项建议将评估全身运动，以理解运动图的意义，重点是体现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，并已被认为值得支持，通过使用基金会的智力优势和更广泛的影响审查标准进行评估。

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.