2026-09-22T22:21:18Zhttps://ubir.buffalo.edu/oai/request

oai:ubir.buffalo.edu:10477/866642025-07-15T18:38:21Zcom_10477_77914col_10477_86252

Development of a Psychophysiological Artificial Neural Network to Measure Science Literacy Kavner, Amanda; 0000-0001-9364-8452 Lamb, Richard Learning and Instruction education neurosciences artificial intelligence Ph.D. The rapid development of other nations' science and technology makes it more difficult to stay competitive internationally without concentrating on how science is taught in US classes. Representative competence, the capacity to generate, transform, interpret and clarify representations, is the primary obstacle to visual literacy in science, technology, engineering and mathematics (STEM) fields and although the relationship between the fundamental visual literacy and domain-specific science literacy is known, how visual science literacy is achieved through science learning is still not fully understood. In order to improve student representational competence skills, the hierarchy of component visualization skills required to interpret these science representations needs to be identified in order to evaluate an individual's level of visual science literacy and to provide the resources to enable the individual to reach the next literacy level. This involves the construction of instruments capable of assessing visual science literacy as well as a Rasch analysis to rank complexities of science visuals. This research investigates modern methods of assessing visual science literacy with a focus on using artificial neural networks (ANN) to analyze neurocognitive measurements captured during science content related tasks and requiring varying predictable levels of visual science literacy. The method of developing this machine learning tool will be detailed by investigating the ANN, successfully made using the Gradient Boosted Trees algorithm to analyze functional Near-Infrared Spectroscopy (fNIR) data. With an autonomic, neurocognitive, and quantitative scientific literacy assessment, educators and curriculum designers will have the ability to create more targeted classroom resources to enhance the visual and spatial cognitive processes behind visual science literacy. **To request an accessible version of the file(s) associated with this item, contact library@buffalo.edu. Please include the item's persistent URL [http://hdl.handle.net/. . .] in your request.** 2025-02-21T21:36:13Z 2025-02-21T21:36:13Z 2020 Text Dissertation http://hdl.handle.net/10477/86664 eng Users of works found in University at Buffalo Institutional Repository (UBIR) are responsible for identifying and contacting the copyright owner for permission to reuse. University at Buffalo Libraries do not manage rights for copyright-protected works and cannot assist with permissions. Copyright retained by author. application/pdf State University of New York at Buffalo