USING COGNITIVE LOAD TO DEVELOP AND OPTIMIZE VIRTUAL ENGINEERING MECHANICS LEARNING EXPERIENCES THROUGH AN EYE TRACKING-BASED FRAMEWORK
Research Poster Engineering 2025 Graduate ExhibitionPresentation by Jackson Robbins
Exhibition Number 146
Abstract
With the growing use of virtual reality (VR) in education and professional training, there is a strong demand to integrate it into undergraduate engineering curricula, especially for foundational courses like Statics and Strength of Materials. The ability to develop mental models to solve problems is a fundamental skill that students must develop in order to succeed not only in these courses, but throughout their engineering education and into their professional career. However, this is often a major challenge for students, especially when problems that require the student to think in three dimensions are accompanied only by two-dimensional figures. Cognitive Load Theory (CLT), which categorizes cognitive load into three types (intrinsic, extraneous, and germane), will be used as the theoretical framework. Unlike most similar studies, which treat cognitive load as a single variable, this project will use eye tracking to separately monitor these three load types and link them to specific eye movement patterns. Additionally, the project will investigate the impact of learner agency in VR experiences by developing three modalities of virtual lessons: a VR video, a guided VR experience, and an unguided, self-directed VR experience. Measuring each type of cognitive load independently through eye tracking will allow for real-time monitoring of students’ cognitive states as they learn in a virtual environment. This work seeks to provide experimental evidence supporting the triarchic model of CLT and inform the design of VR learning environments for engineering education.
Importance
As VR becomes more ubiquitous in educational settings, it is crucial to ensure that students get the most out of this novel technology. Exploring engineering mechanics problems in a virtual environment significantly aids students' ability to visualize and understand them. By using eye tracking to compare the cognitive states of students as they undergo virtual lessons with varying degrees of learner agency, we hope to encourage developers of similar VR experiences to tailor the level of participant engagement appropriately.