Quantifying Two Dimensional (2D) and Three Dimensional (3D) Anatomical Learning Using a Neuroeducational Approach

Abstract

BackgroundAdvances in computer visualization enabling both 2D and 3D representation have generated tools to aid perception of spatial relationships and provide a new forum for instructional design. To date, studies examining the effectiveness of these educational tools have been comparative, using performance measurement as proxy variables for learning. A key knowledge gap in the field of health professional education is the lack of understanding of how the brain processes and learns from spatially presented content.ObjectiveTo use a reinforcement‐based learning activity to assess learning with 2D and 3D (stereoscopic) anatomical representations by comparing event‐related brain potentials (ERPs) as measured by electroencephalography (EEG).MethodsMean ERP waveforms of two ERP components, N250 (related to object perception) and reward positivity (related to learners responding to positive feedback), were compared as novice participants (n = 61) learned to identify and localize neuroanatomical structures. Participants learned from 2D, 3D or a combination of 2D and 3D models.ResultsN250 is significantly greater when participants view 3D versus 2D represented anatomical images. Behavioural learning curves and reward positivity did not differ based on model type used during initial learning. However, interleaved learning incorporating 2D and 3D models provided an advantage in retention and transfer activities represented by decreased reward positivity.ConclusionDespite the lack of difference in behavioural‐based learning efficiency outcomes for 2D versus 3D models, neural measures reveal new insights. Greater object recognition was noted for participants learning from 3D models and interleaved training using both 2D and 3D model types provides advantages for memory retention. These new insights should be kept in mind as educators are designing learning activities in the anatomical sciences. Validation of quantitative neurophysiological variables that measure learning will enable a direct measure of knowledge acquisition that can be used to strategically assess and optimize new forms of teaching, learning, and evaluation.Support or Funding InformationThis research was supported by University of Calgary grants (competitive) awarded to the authors including: Teaching and Learning Grant; University Research Grants Committee (URGC) Seed Grant; and the Data and Technology Fund. SA would like to acknowledge scholarship funding provided by: Social Sciences and Humanities Research Council (SSHRC) Doctoral Fellowship; Alberta Innovates Health Solutions (AIHS) Graduate Studentship, and the Queen Elizabeth II Graduate Doctoral Scholarship.This abstract is from the Experimental Biology 2018 Meeting. There is no full text article associated with this abstract published in The FASEB Journal.