Terrell, CassidyLawrence, Andrea E.2021-10-202021-10-202021-10-18https://hdl.handle.net/11299/225061Biochemistry as a discipline can be incredibly difficult to understand as it heavily relies on visual representations of systems of data to portray information, and these representations can be complex and difficult to understand. The cognitive load theory suggests that students use frameworks formed with pre-existing knowledge to process new knowledge, but this process can be easily disturbed by the presence of extraneous cognitive load. Improving students’ visual literacy skills can lessen their cognitive load as it becomes easier to decode and interpret external representations. There is little existing research that addresses how students interpret and store biochemical information in their memory. Studies performed with undergraduate chemistry students tested students’ knowledge of topics by asking them to rank the relatedness of different chemistry-related words/phrases. Our study tests students’ understanding of biochemistry topics by asking them to rank the relatedness of different visual representations instead of words/phrases. The technique used in this study observes how participants organize sets of biochemistry topics by numerically determining the relatedness of two images that are representative of important concepts that are involved in protein structure. Important variables that will be analyzed are the correlation, path length correlation (PLC), and network similarity (NS), all of which will be derived via Pathfinder. Correlation is the consistency of each participant’s data set, PLC and NS both measure the similarities of each participant’s network with the average expert network. The findings of this study can be useful in understanding how students at different levels throughout their academic career organize biochemistry topics within their memory. This will be helpful for structuring classes for optimal learning and retention of information. The findings from this stage of data collection indicate that there is a difference between the neural networks of expert participants and student participants.enPresentation