Evaluation of cognitive load for a mixed reality anatomy application

• Main Authors: Connor Michalski (Author), Melissa Cowan (Author), Jonathan Bohinsky (Author), Ryan Dickerson (Author), Jeffrey H. Plochocki (Author)
• Format: Book
• Published: Elsevier, 2023-06-01T00:00:00Z.
• Subjects: article
• Online Access: Connect to this object online.
• Notes: 2214-854X; 10.1016/j.tria.2023.100247
• Keywords: Mixed reality; Cognitive load; Anatomy education; Medical education; Human anatomy; QM1-695
• Source: Translational Research in Anatomy, Vol 31, Iss , Pp 100247- (2023)

Background: Asynchronous learning is becoming more prevalent in medical education. Mixed reality applications are interactive tools that may be used to study 3D anatomical relationships outside of the classroom. However, learners may face challenges when using new technologies that are unfamiliar to them and require significant mental effort that distracts from learning. This study investigates learner cognitive load for a mixed reality (MR) application developed as an asynchronous resource to study heart anatomy subsequent to classroom learning. Methods: The Cognitive Load Component questionnaire was administered to first-year medical students (n = 18) to measure intrinsic, extrinsic, and germane cognitive load. Responses were collected after classroom and laboratory instruction on heart anatomy and repeated after learners completed an MR-based learning activity asynchronously on the same topic. Results: Cognitive load differed between classroom and MR-based learning. Classroom learning was reported to be more difficult and complex and required greater intrinsic cognitive load. Learners found the MR activity to be more relevant to their studies and expressed reduced extrinsic load in comparison to classroom learning. Learners indicated the MR application had high germane load, allowing for a greater focus on their learning, but the volume of learning was greater in the classroom and laboratory. Conclusions: Asynchronous MR instructional activities are a viable tool to supplement synchronous learning of 3D structure-function relationships of the heart without overburdening cognitive resources of the learner.