<oai_dc:dc xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/ http://www.openarchives.org/OAI/2.0/oai_dc.xsd"><dc:title>Accessing Learning Efficiencies of Using 3D Models in Students' Spatial Understanding while Learning Engineering Subjects</dc:title><dc:creator>Smith, Micah </dc:creator><dc:subject>Vector Mathematics</dc:subject><dc:subject>Education Psychology</dc:subject><dc:subject>Cognitive Load Theory</dc:subject><dc:subject>3D Viewport</dc:subject><dc:subject>Learning Efficiency</dc:subject><dc:subject>Engineering Mathematics</dc:subject><dc:subject>Improving Teaching Resources</dc:subject><dc:coverage>Engineering Science</dc:coverage><dc:relation>B S</dc:relation><dc:description>
3D vector mathematics is a foundational component of the engineering curriculum but is often one of the most challenging forms of mathematics for students to learn, as problems often require visualization in three dimensions. Current teaching methods for 3D mathematics rely on a traditional lecture style and 2D representation, which can make it difficult for students to connect the visual and mathematics concepts. Prior research projects have addressed this issue, but focus on the performance of participants, rather than the learning efficiency. Learning efficiency is related to maximizing the amount learned while minimizing the amount of work, energy, and time required to learn it. This study investigates the problem of learning efficiency through the lens of cognitive loads.
The proposed teaching implement is an interactive 3D model that participants can access on their personal laptop or phone. Eighteen participants were divided into two groups and completed an assessment with either an interactive 3D model or a static 2D image. A post-assessment questionnaire measured cognitive load. Results suggest that 3D models have promise in improving learning; however, the small sample size prevents any definitive conclusions.</dc:description><dc:contributor>Gary L. Gray, Thesis Supervisor</dc:contributor><dc:contributor>Adomas Povilianskas, Thesis Honors Advisor</dc:contributor><dc:rights>restricted_to_institution</dc:rights><dc:date>2026-04-07T22:35:29Z</dc:date><dc:identifier>https://honors.libraries.psu.edu/catalog/10079mas7893</dc:identifier></oai_dc:dc>