The Use of Biometrics and Rubric Analysis to Assess How Students View, Cognitively Process, and Respond to 3D Physical Molecular Models in Undergraduate Biochemistry

Abstract

Among the STEM disciplines biochemistry curricula present a particular challenge to students with abstract concepts that can lead to rampant and resistant misconceptions impeding learning and therefore impact retention. Moreover, research has shown two‐dimensional representations not only fail to effectively convey biochemical concepts, but also propagate misconceptions. In particular, research has shown students have significant difficulty understanding enzyme‐substrate relationships. One way instructors have suggested to support students while learning this content is through the use of 3D physical models of the enzyme and substrate. However, little research has been conducted on how the models impact the learning process and how to best design these models and corresponding activities. 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. These results offer insights into how students view and cognitively process activities using physical models with respect to how the instructor intends students to learn with and use these tools. 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.Support or Funding InformationThis project is supported by the National Science Foundation under award number IUSE 1711402/1711425 to University of Minnesota, Rochester and Kennesaw State University.