Work in Progress: Physiological Assessment of Learning in a Virtual Reality Clinical Immersion Environment

Authors:
raw_author_name: Christine King
raw_affiliation_string: University of California, Irvine
raw_author_name: Kit Feeney

Abstract: Abstract Personalized learning is one of the 14 grand challenges for engineering in the 21st century identified by the National Academy of Engineering. Education is shifting from a one-size-fits-all approach to a personalized process in which learning is tailored to a student's individual needs and learning abilities. In addition to the need for personalized education, the CHIPS and Science Act has listed relevant and experiential training as a strategic objective for US educational institutions. In biomedical engineering programs, this includes the need to perform clinical immersion, or learning from synergistic transactions between people and the environment within a healthcare setting. However, due to a lack of access to nearby medical centers, access for non-essential personnel enter hospitals, and increasing engineering class sizes, the ability for engineering students to shadow physicians has become difficult. To be able to allow students to perform clinical immersion without physically entering a hospital, the Department of Biomedical Engineering at the the University of California Irvine (UCI) has developed a virtual reality clinical immersion platform for student learning of unmet clinical needs and medical device design. This platform and the corresponding learning modules were piloted in Spring of 2022 as a junior-level unmet needs finding course, where students utilized the virtual reality clinical procedures to identify and screen unmet clinical needs using learning methods adopted from the Stanford BioDesign Process, and determine a proposed innovation for the problem that can be designed and validated in their senior capstone course the following year. A virtual reality platform was utilized over video recordings for the program, as prior research suggests that virtual reality for student learning can have a higher positive effect on knowledge transfer and self-efficacy than video recordings. To assess whether knowledge transfer is occurring within the virtual reality clinical immersion platform, and further develop the efficacy of the virtual reality clinical immersion experiences, the authors are developing a phenomenological framework that has been previously utilized in other virtual reality research applications. To this end, the developed framework analyzes the attention to interactions between the users and the virtual environment they are immersed in. To test the framework, student volunteers enrolled in the Spring of 2023 offering of the unmet clinical needs finding course will complete questionnaires regarding their experience engaging with the virtual content before, during, and after taking the course alongside participating in one-on-one interviews (UCI IRB Exempt No. 904). To analyze their phenomenological responses to the virtual environments, the students will also perform the virtual reality experiences while their visual attention data is collected through eye tracking within a virtual reality headset (HP Omnicept Reverb G2). This information will be used to detect what and for how long the student is looking at in the environments. In addition, electroencephalogram (EEG) recordings (Neuroscan 19-Channel Quik-Cap, Biopac MP 160 Data Acquisition System) will be synchronized with the eye tracking information to assess whether there are neurological correlates of learning occurring within the environment using known EEG correlates of learning based on prior research findings. By comparing the physiological correlates of attention and learning against the course learning outcome assessments, such as a laboratory innovation notebook, the authors hope to be able to develop real-time interventions based on the individual user's physiological patterns of learning to improve innovation and unmet clinical needs finding skills within virtual reality environments.