Visualizing Nucleic Acids: Comparing Neural Networks of Undergraduate Students in Molecular Life Science Courses
The intentional development of undergraduate students’ visual literacy skills has the potential to increase conceptual understanding while also decreasing cognitive load. To date, there are few tools for educators to assess students' visual literacy in undergraduate molecular life science courses. This study aims to develop a tool that measures the structural knowledge of students with respect to the vertical translation visual literacy skill using nucleic acid structure representations. The visual literacy neural networks of students from a large R-1 university were compared to a small, primarily undergraduate institution (PUI) to answer the question: Does the amount of exposure to biology, chemistry, and biochemistry courses influence student’s neural networks? The Pathfinder program generated a neural network image, and a coherency, path length correlation (PLC), and network similarity (NS) value for each participant. Student data were calculated against an established expert reference network and RStudio was used for statistical analysis of these variables. Our preliminary results compare these three variables between students in a biochemistry course at an R-1 university to those from a PUI. These data could be used to design curricula that help students learn and store information more effectively.