Accessing The Development Of The Horizontal Translation Visual Literacy Skill In Students Using Neural Networks

Abstract

Research shows that many students struggle with visual representations in molecular life science courses, which impacts their ability to learn the material. Currently, there is minimal research related to the development of students’ visual literacy neural networks. This study aims to understand how students’ neural networks evolve based on their chemistry and biochemistry course enrollment. For this study, students as well as experts took a survey in which their horizontal translation visual literacy skills were measured to make neural networks. Then, the students’ neural networks were analyzed across the chemistry and biochemistry curricula and compared to experts’ neural networks in order to answer the question: How do the neural networks of students change throughout the different curricula in comparison to experts? Utilizing Pathfinder, eccentricity values for each node were generated in which a low value signifies a node(s) is the most central node(s). Furthermore, the degree values indicate which node(s) has the highest degree of branching. With both these values, they can be used to look at whether the neural network of students are similar or different from the experts. These data could help create a curriculum for chemistry and biochemistry courses that could possibly improve students’ visual literacy skills.