Building an AR Magnetic Field Visualizer—Rationale
Technology gives humans the opportunity to perceive physical attributes of the world that are completely invisible. Magnetic fields, for instance, are not directly observable, but being able to visualize them is important for understanding experiences as commonplace as transmitting radio signals to a car stereo system or as cutting-edge as doing atomic particle research. Personal mobile devices (PMDs) such as smartphones and tablets are enabled with sensors that could make the ability to visualize invisible fields a reality for anyone who has access to a PMD, including the 77% of U.S. adults ages 18+ who own a smartphone (Rainie, L. & Perrin, A., 2018). For the 92% of smartphone-owning U.S. young adults ages 18-29, the ability to use PMDs to make sense of physical data has the potential to go far beyond using Google Maps. By developing recently-released software frameworks for augmented reality (AR) and integrating them with sensors, PMDs have the potential to become integral tools for the teaching and learning of highly abstract concepts and visualization skills both in formal education as well as a tool within the workplace.
Fields and the ability to perceive them are essential for the education of a technology-enabled future workforce. In the Framework for K-12 Science Education, the nation’s leaders of science, technology, engineering, and mathematics (STEM) and STEM education recognized the importance of the inclusion of fields (electric, magnetic, and gravitational) as an underlying disciplinary core idea that all children should learn (National Research Council, 2012). The ability of computers to perceive sensory data has likewise been recognized as a future technology trend that is likely to have a significant impact on the U.S. workforce (National Academies, 2017, pg. 50).
Our work with AR is ultimately in service of creating a 3-D visualization tool that will allow users to connect magnetometer data with a visual grid, allowing them to see and interact with vectors, field lines, or contour lines around a real magnet.
In our previous two posts, we compared two AR frameworks, Google ARCore and Apple iOSKit, based on their accessibility and user base and core functionality.
Preparing students in the physical sciences is one gateway to a technologically-literate workforce. There were 8.6 million STEM jobs in the U.S. in 2015, with over 69% of those jobs in the areas of computing, engineering and the physical sciences (U.S. Bureau of Labor and Statistics, 2017), many of which rely on students who have earned degrees in physics (American Institute of Physics, 2017) or for whom physics has been a significant part of their coursework. There is also an expectation that there will be above-average growth in many STEM occupations, most notably in STEM postsecondary teaching and physical sciences in industry.
High school and introductory college physics is where many students—including women—commit to the physical sciences for their careers. Ensuring that students have the opportunity to comprehend difficult physical science concepts, such as fields, is exceptionally important for equity. In a survey of 962 female physicists, a 52% made their decision to pursue the field between the beginning of high school and the beginning of college, suggesting that positive high school experiences with physics are pivotal (Hazari et al., 2017). It follows that investment in technology that gives insight into educational difficulties in physics might result in better teaching practices and resources that support students’ acceptance of STEM careers.
Fields are ubiquitous in STEM learning and careers. Field visualizations are essential to many branches of the workforce. Magnetic fields, in particular, are used in tasks as diverse as mapping geological magnetic fields for coal mining, studying high-energy particles in accelerators, manufacturing hybrid vehicles, doing biomedical imaging, studying space weather, and doing the daily work of electricians and plumbers. In its recent report (National Research Council, 2013), the National Academies of Engineering recognized the impact of high magnetic fields to fundamental science and beyond, and made a plea for a “highly trained workforce that is specialized in the knowledge and expertise of magnet design and construction” and states that “there is no unified structure to this type of learning, and often what is learned may be particular to a specific magnet application, and thus highly specialized and narrow in scope” (p. 164).
Broader understandings about magnetic fields and their interactions with electric fields - and not just specialized understandings about a specific tool - are necessary for a well-prepared workforce. Participants in the workforce might engage with magnetic fields through their jobs in ways as simple as a technician using a meter to measure the flow of water past a spinning magnetic gauge attached to a pipe, or as complex as modeling dipole, quadrupole (Zhang, et al., 2011), or octupole magnetic fields (Baumgartner et al., 2017, unpublished) used to accelerate particles for the study of physics. Given this prevalence of magnetic fields in careers, individuals in the trades as well as those with advanced science or engineering degrees are just as likely to encounter fields in their daily work. Thus both the College Board Standards for College Success (College Board, 2009) and the more recent Next Generation Science Standards (NGSS Lead States, 2013) emphasize the field concept, and tie other essential understandings (such as conservation of energy) to the reality of fields as an outcome for all students completing high school instruction.
A Case Study in Understanding Fields: Understanding the 3-D nature of magnetic fields and the relationship to electric currents is often difficult because fields are intangible and require high-level visualization and spatial reasoning skills. One study of student understanding of 3-D magnetic fields produced by a complex current system involved eight physics majors. The students included a graduate student who had significant prior exposure to traditional instruction about magnetic fields in general, and who had also received instruction about the particular current system in question through reading, lectures, and their research projects (Lopez & Hamed, 2004). However, when given a 2-D printed image of the current system, they were unable to accurately describe the magnetic field perturbation produced by the current system, even though all of the students demonstrated a clear understanding of the underlying physics of the relationship of magnetic field and current (the Biot-Savart Law). However, when viewing the current system as a 3-D computer visualization that could be manipulated with rotate and zoom, all of the subjects were able to correctly describe the perturbations. An analysis of the interviews of the students demonstrated that the manipulation of the spatial information through mental images was producing too much cognitive load, which resulted in failure to perform the task. The visualizations allowed the students to correctly integrate the spatial information into the physics they understood and then to correctly solve the task. The authors conclude that “the use of 3-D images could be a very important pedagogical tool in introductory physics courses when students first encounter the subject of magnetic fields and their relationship to electric current.”
Few technological solutions exist to help probe students’ difficulties with fields or to make instructional improvements that address the needs for 3-D visualization. Despite the robust research on physics education and visuospatial skills generally, limited research has been done on students’ conceptions of 3-D fields or their interactions. Typically, instruction and research has been limited to 2-D representations of fields and problems that can be solved analytically, while the real world problems that the future workforce will encounter are much more likely to require analysis of complex fields that can only be solved through 3-D modeling.
Why Sensor-Based Visualization Technology?
The computational perception of sensory data has been recognized as a future technology trend that is likely to have a significant impact on the U.S. workforce (National Academies, 2017, pg. 50).. Augmented reality (AR) technology on personal mobile devices (PMDs) is one possible solution to addressing the need to support education in preparing a future workforce to work with technology and complex data about fields. At the most fundamental level, AR provides users of PMDs with a new window into reality, allowing them to see things that are invisible to the unaided eye. With magnetic fields, AR can allow users to observe real magnetic objects in their environment, and, using internal sensors, superimpose a visualization of their surrounding fields and interactions between two or more fields when multiple magnets are present. Diagram #1 shows a simplified
mock-up of an idealized, isolated 3-D magnetic field visualization around a bar magnet. Magnetism is just one specific area in which field visualization has high potential to support learning; sound, light, and radio/microwave emissions can also be sensed by PMDs and visualized as fields. Diagram #2 is a simplified mock-up of interfering sound waves produced by two speakers. Although sound fields are not within the scope of this proposed project, advancement of AR technology that senses magnetic field data could be expanded in the future to include other field data. (See enlarged images in the Supplementary Document titled “Diagrams”).
This exploratory project proposes to develop new 3-D, AR visualization tools with PMDs such as smartphones and tablets, with the goal to enhance introductory physics learners’ understanding of physical fields through a visualization of 3-D, real-time, sensor-based magnetic fields. In September 2017, new, public AR frameworks for PMDs were released openly to developers: ARKit for iOS and Google ARCore for Android. Linking the PMDs’ internal motion sensors and camera algorithms, these new frameworks allow developers to create AR worlds in which the PMD has a 3-D environmental awareness, allowing users to not only display images on a 2-D background, but to interact with them in a 3-D environment without the use of cumbersome, pre-made visual targets.
PMDs are ubiquitous among students and workers of nearly any age, gender, and socioeconomic status. Never in the history of education or technology has there been a device like the smartphone that has placed so many diverse learners on such equal footing. Technological advancements on PMDs are accessible to nearly everyone--either through direct ownership or through association with family members and friends who own them--and provide a unique opportunity to close the gap in terms of access and experience with physics. Pew Research Center (2017) reports that males and females ages 18+ own smartphones at nearly same rates (78% and 75%, respectively), and almost no gap exists between smartphone ownership by African Americans, Hispanics, or Whites (72%, 75%, and 77%, respectively). While there are greater differences in smartphone ownership based on income (64% for adults with an income less than $30K compared to 93% for adults with an income more than $75K) and geography (67% for rural and 79% for suburban), the high percentage of smartphone users overall suggests that a significant majority of the American population has a personal connection to smartphone. Additionally, 51% of U.S. adults own a tablet, which often has similar sensor and visualization capabilities of a smartphone. Smartphone ownership and usage begins early. Pew found that members of their youngest studied age group (ages 18-29) have the highest smartphone ownership (92%). An earlier report by Pearson (2015) showed that 82% of high school students regularly use a smartphone.
Technology Is Not Yet Available to Fill Educational Needs
New software to enhance PMD capabilities can allow us to learn how to prepare students for real-world problems faced by the future workforce. The power of PMDs for raw data collection has been recognized in science research (Cartwright, 2016) and engineering (Alexander, 2015). Further, of the 1M+ users of our popular free Physics Toolbox Sensor Suite, a significant percentage self-identify as engineers/researchers (37%) or technicians (electrical, automotive, aeronautical, theater, urban planning) (10%). Free Physics Toolbox apps are not only being used casually, but have demonstrated their utility in diverse, peer-reviewed research findings, such as the use of sensors to economically identify enzymatic reactions outside of the lab (Zarzar et al., 2017), improve the design of optical gyroscopes (Srivastava et al., 2016), analyze human social behavior through movement (Ellamil et al., 2016), and design user interfaces for interactive displays on flight decks (Avsar, 2015), among many others. The potential for 3-D AR visualization of magnetic fields, specifically, is highly likely to add value for many researchers.
The connection between the workforce and education is particularly clear with sensor-based PMD apps, as nearly an equivalent number of Physics Toolbox users report being engineers, researchers, or technicians (47% total) as the total percentage of users in education (42% total), which is broken down into university or high school teachers (27%) or students (15%). The fact that data collection apps on PMDs are being used across education and into the workforce make them an especially appropriate type of technology in which to make further developmental investments. Early work by educators trying to teach students about magnetic resonance imaging (MRI) techniques demonstrates the need for hands-on, interactive engagement, and provide early evidence that realistic experiments with PMD sensors lead to better learning than the current trends toward pure simulation (Benli et al., 2015). There is also great interest among introductory physics educators in utilizing magnetic field detectors, if only to collect and analyze raw, single-dimension data to perform activities such as modeling the inverse-cubed relationship of magnetic field intensity and distance from the source (Arribas et al., 2015) or measuring the Earth’s magnetic field dip angle (Arabasi & Al-Taani, 2016). Enabling 3-D visualization of magnetic fields will open up new opportunities for discipline-based education research (including fields beyond magnetic, such as sound and light fields) and general visuospatial reasoning skills research.
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This work is funded by NSF Grant #1822728. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.
