Iterative Design of 3D Physical Serine Protease Models based on Biometric Data to Optimize Cognitive Load and Decrease Misconceptions in Undergraduate Biochemistry

Abstract

Biochemistry students misconceptions related to enzyme‐substrate interactions can stem from prior knowledge in biology and chemistry where this and related concepts are inadequately represented visually, explained and/or assessed. Moreover, research has shown two‐dimensional representations not only fail to effectively convey biochemical concepts, but also propagate misconceptions. We hypothesize that three‐dimensional (3D) physical models used in conjunction with targeted active learning assessments will increase student understanding of shape, stereochemistry, and electrostatic interactions involved in enzyme‐substrate interactions. However, little research exists on how to design 3D physical models with corresponding assessments that optimize the cognitive load of the student which would enhance learning. This presentation will describe an iterative design process based on biometric, observational and rubric data analysis. The biometric data is collected using electroencephalographic (EEG) and eye tracking tools and voice recordings from simulated learning environments where biochemistry students completed a serine protease activity with corresponding physical models. In addition, observational and rubric analyses of student responses to activities from the simulated learning environment and real classroom sessions provide further data for the iterative activity design process. Here, we will offer suggestions for faculty interested designing 3D physical models with corresponding active learning assessments that optimize the cognitive load and target student misconceptions.This abstract is from the Experimental Biology 2019 Meeting. There is no full text article associated with this abstract published in The FASEB Journal.