Collaborative Research: Seeing Science: Using Computer Vision to Explore the Scientific Principles Behind Everyday Objects

理解科学对于培养学生理解周围世界的意义、做出明智的决定以及参与公民社会和劳动大军至关重要。然而，对于许多年轻人来说，科学是一个神秘的知识体，感觉与他们的生活脱节。该项目旨在将科学带入中学生的家中，让他们看到日常物品背后的科学，并将生活环境转变为引人入胜的学习空间。学生们将在手机上的探究式学习单元中探索STEM现象，如扩散、电力和出现在他们厨房、卧室和当地公园的简单机器。他们将能够拍摄他们的家和邻居的照片和视频，计算机视觉算法将通过图表、模型和模拟来增强这些图像，这些图表、模型和模拟说明了解释STEM现象的原理和机制。这些覆盖物将允许学生观察、实验和预测茶叶在热水中扩散或热量通过墙壁传播等现象。该项目将利用现有的技术设备，如照相手机，创造出“透镜”，使学生能够看到他们周围的科学。通过致力于这种低成本的解决方案，该项目旨在让更多的学生获得科学教育，并在避免家庭资源过度压力的同时，实现在家学习。在多样化、低资源的环境中运行，推动了计算机视觉的根本进步：首先，算法必须自动建立给定物理现象场景的3D和时间表示。其次，系统必须显示挂钩，以便教育人员决定在什么时间、在场景顶部的哪个位置应该覆盖哪些图形。此外，该项目将致力于以人为中心的设计，以便于获得、易于使用并实现教育目标的方式将尖端技术带给年轻人。调查人员将对家长、学生和教师进行广泛的访谈，了解他们愿意与研究人员、同龄人和教师分享学生校外生活的哪些方面。这些数据将使该团队能够实现建立在学生生活和文化基础上的公平科学教育的好处。这项研究将有助于促进在家中从事科学研究的更具代理、更具包容性的方式的发展，鼓励学生从小就与科学建立个人联系。这对于那些认为科学与他们的生活脱节的风险最大的学生来说尤其重要，他们可以从生活和社区中看到科学的工作中受益最大。这一奖项反映了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。

Understanding science is critical for preparing students to make sense of the world around them, make informed decisions, and participate in civic society and in the workforce. However, for many youth, science is a mysterious body of knowledge that feels disconnected from their lives. This project aims to bring science into middle school students’ homes, allowing them to see the science behind everyday objects and transforming lived environments into engaging learning spaces. Students will work on inquiry-based learning units on mobile phones that explore STEM phenomena topics like diffusion, electricity, and simple machines that are present in their kitchens, bedrooms, and local parks. They will be able to take photos and videos of their home and neighborhood, and computer vision algorithms will augment these images with diagrams, models, and simulations that illustrate the principles and mechanisms that explain the STEM phenomena. These overlays will allow students to observe, experiment with, and make predictions for phenomena such as tea diffusing in hot water or heat traveling through walls. The project will capitalize on existing technological devices, such as camera phones, to create “lenses” which enable students to see the science that is all around them. By committing to such low-cost solutions, the project aims to make science education accessible to more students, and enable at-home learning while avoiding undue pressures on family resources. Operating in diverse, low-resource environments motivates fundamental advances in computer vision: First, algorithms must automatically build up a 3D and temporal representation of a scene of a given physical phenomenon. Second, the system must expose hooks for educators to decide which graphics should be overlaid at which time and in which place atop this scene. Further, the project will engage in human-centered design to bring cutting-edge technologies to youth in ways that are accessible, easy to use, and achieve educational goals. Investigators will conduct extensive interviews with parents, students, and teachers about the aspects of students’ out-of-school lives that they would be willing to share with researchers, peers, and teachers. These data will enable the team to realize the benefits of equitable science education that builds on students’ lives and cultures. This research will help foster the development of a more agentic, inclusive way of engaging in science inquiry at home, encouraging students to have a personal connection with science from a young age. This is particularly important for students most at risk to perceive science as disconnected from their lives, and whom can benefit most from seeing science at work in their lives and community.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.