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Smartphones may be seen as miniature toolboxs to perform Physics experiments. In this paper, we present three different optics workbenches mainly based on the light meter of a smartphone. One is aimed at the precise study of Malus law and other effects associated to linearly polarized light, the second allows quantifying the light intensity distribution of diffraction or interference patterns projected on a screen, and the third demonstrates the so-called inverse square law obeyed by the light from a pointlike source. These experiments allow to quantitatively demonstrate three fundamental laws of optics using quite inexpensive equipment.
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The efficiency and accuracy of mapping are crucial in a large scene and long-term AR applications. Multi-agent cooperative SLAM is the precondition of multi-user AR interaction. The cooperation of multiple smart phones has the potential to improve efficiency and robustness of task completion and can complete tasks that a single agent cannot do. However, it depends on robust communication, efficient location detection, robust mapping, and efficient information sharing among agents. We propose a multi-intelligence collaborative monocular visual-inertial SLAM deployed on multiple ios mobile devices with a centralized architecture. Each agent can independently explore the environment, run a visual-inertial odometry module online, and then send all the measurement information to a central server with higher computing resources. The server manages all the information received, detects overlapping areas, merges and optimizes the map, and shares information with the agents when needed. We have verified the performance of the system in public datasets and real environments. The accuracy of mapping and fusion of the proposed system is comparable to VINS-Mono which requires higher computing resources.
Interest in building dedicated Quantum Information Science and Engineering (QISE) education programs has greatly expanded in recent years. These programs are inherently convergent, complex, often resource intensive and likely require collaboration with a broad variety of stakeholders. In order to address this combination of challenges, we have captured ideas from many members in the community. This manuscript not only addresses policy makers and funding agencies (both public and private and from the regional to the international level) but also contains needs identified by industry leaders and discusses the difficulties inherent in creating an inclusive QISE curriculum. We report on the status of eighteen post-secondary education programs in QISE and provide guidance for building new programs. Lastly, we encourage the development of a comprehensive strategic plan for quantum education and workforce development as a means to make the most of the ongoing substantial investments being made in QISE.
Homework grading is critical to evaluate teaching quality and effect. However, it is usually time-consuming to grade the homework manually. In automatic homework grading scenario, many optical mark reader (OMR)-based solutions which require specific equipments have been proposed. Although many of them can achieve relatively high accuracy, they are less convenient for users. In contrast, with the popularity of smart phones, the automatic grading system which depends on the image photographed by phones becomes more available. In practice, due to different photographing angles or uneven papers, images may be distorted. Moreover, most of images are photographed under complex backgrounds, making answer areas detection more difficult. To solve these problems, we propose BAGS, an automatic homework grading system which can effectively locate and recognize handwritten answers. In BAGS, all the answers would be written above the answer area underlines (AAU), and we use two segmentation networks based on DeepLabv3+ to locate the answer areas. Then, we use the characters recognition part to recognize students answers. Finally, the grading part is designed for the comparison between the recognized answers and the standard ones. In our test, BAGS correctly locates and recognizes the handwritten answers in 91% of total answer areas.
Commercial video games are increasingly using sophisticated physics simulations to create a more immersive experience for players. This also makes them a powerful tool for engaging students in learning physics. We provide some examples to show how commercial off-the-shelf games can be used to teach specific topics in introductory undergraduate physics. The examples are selected from a course taught predominantly through the medium of commercial video games.
This article reports on a study investigating how computational essays can be used to redistribute epistemic agency--cognitive control and responsibility over ones own learning--to students in higher education STEM. Computational essays are a genre of scientific writing that combine live, executable computer code with narrative text to present a computational model or analysis. The study took place across two contrasting university contexts: an interdisciplinary data science and modeling course at Michigan State University, USA, and a third-semester physics course at the University of Oslo, Norway. Over the course of a semester, computational essays were simultaneously and independently used in both courses, and comparable datasets of student artifacts and retrospective interviews were collected from both student populations. These data were analyzed using a framework which operationalized the construct of epistemic agency across the dimensions of programming, inquiry, data analysis and modeling, and communication. Based on this analysis, we argue that computational essays can be a useful tool in redistributing epistemic agency to students within higher education science due to their combination of adaptability and disciplinary authenticity. However, we also argue that educational contexts, scaffolding, expectations, and student backgrounds can constrain and influence the ways in which students choose to take up epistemic agency.
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