Novel Observations in Mixed Reality (NOMR): Designing a New Frontier of Physics to Practice Generating Scientific Models
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Canright, Jared Phelps
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Abstract
The creation of new knowledge in the form of scientific models is a cornerstone of the process of science. In physics laboratory instruction, students are very often stuck in a confirmatory mindset; they have been conditioned to believe that the role of experimentation in the classroom and in the world is to verify known facts. This mindset is antithetical to the actual practice of science and detrimental to students' growth as scientific thinkers.
Freeing students from such a mindset is a central challenge in the development and implementation of epistemologically authentic physics laboratory activities. This dissertation describes the exploration and implementation of a unique solution to this challenge: The complete removal of any and all traces of a ``correct'' answer through immersion in a different universe with its own laws of physics. The Novel Observations in Mixed Reality (NOMR) project simulates real and fictitious laws of physics in a virtual reality universe to create learning opportunities where the only right answer is the one that students can make the best case for. NOMR's physics focus around field-mediated particle interactions: Familiar ones such as electrostatics and Newtonian gravitation, and novel ones created for students to explore from scratch. This dissertation reports on the development of the VR software, fictitious physical phenomena, classroom implementation practices, and instructional materials comprising NOMR labs. These labs are implemented across four large-enrollment courses on two WA campuses, reaching over 2,000 students each year. Assessment of NOMR's effects on affective components of students' learning reveals that students become more expertlike in their epistemology and self-efficacy about experimental physics, and that they become more engaged with successive NOMR labs even beyond the expiration of the VR novelty effect. NOMR has been shown to be feasible and work as intended; it is primed for the next generation of developments and inquiry, building on the foundation presented here.
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Thesis (Ph.D.)--University of Washington, 2023
