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

将智能手机光检测和测距与增强现实相结合，增强 STEM 中基于位置的教学

基本信息

批准号：
2114586
负责人：
Colleen Megowan-Romanowicz
金额：
$ 57.35万
依托单位：
American Modeling Teachers Association
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2021
资助国家：
美国
起止时间：
2021-10-01 至 2024-09-30
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2114586&HistoricalAwards=false
关键词：
Combining Smartphone Light Detection Ranging

项目摘要

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.

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，特别是在物理和数据科学领域。教育研究人员提倡采用多种方法来支持运动数学模型的意义建构，但教师往往很难做到这一点。由于资源有限，该项目将通过基于光的移动传感器（LiDAR）来检测人与物体的距离以及空间内的位置，从而使任何拥有智能手机的人都能实现高精度位置传感。教育研究将衡量通过制作可供教师和学生在传统教室以及家庭中使用的课堂应用程序和游戏化课程，使用这项新技术来提高学生对动态图数学模型的学习和参与度。开发人员将基于 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颖的背面红外光束阵列显着提高位置测量和放置的精度。增强现实（AR）该项目将确定激光雷达辅助 AR 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。学习科学研究需要与 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。将评估全身运动通过重点可视化体现数据素养的学习和实践来理解动态图。该奖项反映了 NSF 的法定使命，并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，被认为值得支持。