Harnessing the Digital Science Education Revolution: Smartphone Sensors as Teaching Tools

Harnessing the Digital Science Education Revolution: Smartphone Sensors as Teaching Tools

Rebecca E. Vieyra (Vieyra Software, USA), Colleen Megowan-Romanowicz (American Modeling Teachers Association, USA), Daniel J. O'Brien (Georgetown University, USA), Chrystian Vieyra (Vieyra Software, USA), and Mina C. Johnson-Glenberg (Arizona State University, USA)
DOI: 10.4018/978-1-6684-5585-2.ch008
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Students regularly struggle with visualization and modeling of physical phenomena. Incorporating smartphones into teaching strategies has the potential to improve learning outcomes while simultaneously augmenting students' digital literacy skills in an applied context. This chapter discusses two smartphone applications developed by the authors and the accompanying research efforts in understanding how students might learn with this technology. The first app pairs augmented reality (AR) with smartphone magnetometers to visualize three-dimensional magnetic fields in space. The second uses light detection and ranging (LiDAR) technology available on modern iPhone models to plot position-time and velocity-time graphs based on users' motions. Each was developed with the support of students, teachers, software developers, and educational psychologists. In this chapter, the authors share their perspectives and other recommendations for the use and development of similar technologies to improve learning.
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The use of digital tools to teach science grew out of educational research on microcomputer-based laboratories (MBLs) (Reddish et al., 1997; Thornton, 1987; Thornton & Sokoloff, 1990). MBLs, produced by companies such as Vernier, PASCO, and Texas Instruments, use specialized software and probeware such as digital motion sensors, force meters, and thermometers to collect data. Research on using these tools demonstrated their pedagogical affordances, including system simplification, real-time data display, automated data collection, increased student control, and easy data display transformation (Anderson & Wall, 2016; Struck & Yerrick, 2010). Further research has focused attention on game-based devices, including those that use embodied motion-capture such as the Wii and mixed reality (Lindgren & Johnson-Glenberg, 2013) and the Hololens (Georgiou & Ioannou, 2019; Walkington et al., 2022). However, MBL technologies are only accessible to students while in the classroom, and game-based technology peripherals like Kinect and head-mounted augmented or video reality displays are rarely available in schools. Further, these technologies are only infrequently used by professional scientists.

Key Terms in this Chapter

Augmented Reality: An interactive technology that overlays computer-generated images atop the real-world environment.

Vector: A quantity having both direction and magnitude, often represented by an arrow whose length corresponds to its magnitude and whose orientation in space corresponds to its direction.

Mobile Devices: A portable form of technology that typically operates through its connection to the internet and/or other sensor-based networks, and includes smartphones and tablets.

Embodiment: In cognitive psychology, a theory that espouses that the body and cognition are inextricably linked and that learning can be both supported by and take place through bodily experiences.

LiDAR: “Light Detection and Ranging,” a remote sensing technology that measures the distance to an object or surface by calculating the time a light pulse takes to reach and reflect back from said object.

Magnetic Field: The space near a magnetic material or moving charge in which magnetic forces act.

Modeling: The creation, refinement, and/or application of representations that illustrate principles or relationships within a system.

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