Articles and Scholarly Works (Rochester)
Persistent link for this collection
Search within Articles and Scholarly Works (Rochester)
Browse
Recent Submissions
Item Weaving Through Neural Webs: Measurement of How Students Connect the Visual Literacy Skill of Horizontally Translating Across a Chemistry Curriculum(2022-12)Biochemistry is an upper-division course that teaches topics using visual representations of systems of data, which can be a challenging way for students to learn. To lessen their cognitive load, students may find that improving visual literacy skills aids their understanding of biochemistry. There is little existing research that assesses how students interpret and store biochemical information and representations in their long-term memory. Previous studies performed with undergraduate general chemistry I students measured structural knowledge, or neural networks, of topics by asking students to assign the relatedness of chemistry-related words/phrases. Our study intends to analyze neural networks of a biochemistry visual literacy skill where undergraduate chemistry and biochemistry students to rank the relatedness of biochemical representations as an alternative to words/phrases. Specifically, this study assesses students' structural knowledge of the horizontal translation visual literacy skill, relating to the oxygen binding concept in comparison to the enzyme-substrate concept. We want to determine if there are pedagogical strategies and/or course instructional modalities that impact students' neural network development toward expert-like organization of the horizontal translation visual literacy skills. Preliminary analyses assess whether students are becoming more expert-like in correlation with their exposure to chemistry and biochemistry concepts. The data from student responses are analyzed in Pathfinder against an expert reference network to generate average degree and eccentricity values, path length correlation (PLC), and neighborhood similarity (NS) values, as well as patterns in organization/chunking. Degree values indicate the most branched nodes, while eccentricity values indicate the most central node in the neural network. PLC indicates how well each node is connected, and NS values are similarities in the grouping of concepts around the central node. Moreover, patterns in organization/chunking allow for similar groupings of nodes to be assessed. Through this study, we hope to improve curricular materials for biochemistry learning, in hopes that students will become more expert-like throughout their chemistry and biochemistry sequence. The outcome of this analysis may aid the improvement of curricular materials for optimal learning and retention of information.Item Accessing The Development Of The Horizontal Translation Visual Literacy Skill In Students Using Neural Networks(2022-09) Andrade, Vanessa A; Prat-Resina, Xavier; Terrell, Cassidy R; Morin, Chloé S; Contreras Vital, Juquila IResearch 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.Item Flipping Information Literacy(2018-03) Sancomb-Moran, Mary E.