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，尤其是在物理和数据科学方面都很重要。教育研究人员提倡通过数学动作模型来支持感知创造的多种方法，但是由于资源有限，教师经常努力制定他们。该项目将使拥有智能手机的任何人通过基于光的移动传感器（LIDAR）构建能够检测到一个人与空间内的物体和位置的距离，这将使高精度位置感知感官。该教育研究将通过生产有关教室就绪的应用程序和Gamidi课程，供教师和学生在传统的教室以及家庭中使用的教师课程，以改善使用这项新技术来改善学生学习和参与数学模型的效果。研究人员和教育软件开发人员将根据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的背面基础设施梁阵列，以显着提高位置测量值的精度，并根据用户的动作和环境数据来实现增强现实（AR）可视化的位置。该项目将确定激光雷达AR技术可以在多大程度上实现高精度，基于位置和实时数据可视化的程度。它将探索新技术如何提供通过图形和向量进行建模运动所需的认知脚手架和体现的经验。学习科学的研究将需要与STEM教育者进行合作，以开发和测试场景在传统和远程学习环境中探索的有效性。该提案将评估全身运动，以使运动图的意义，重点是具有数据可视化素养的体现学习和实践。该奖项反映了NSF的法定任务，并通过使用基金会的知识分子优点和更广泛的影响评估标准来通过评估来评估。