Assistant Professor Biography Brandon was born and raised in Michigan. He earned sequential B.S. in Chemistry and M.S. degrees from the University of Michigan while conducting research with Theodore Goodson III before moving to the Georgia Institute of Technology where he received his M.S. degree in inorganic chemistry with Henry La Pierre. Brandon earned his Ph.D. in Chemistry with a focus on chemistry education research under the supervision of Jeffrey Raker at the University of South Florida. His graduate research focused on the development of machine learning- and research-based assessment tools for the learning of organic chemistry reaction mechanisms and evaluating research-based instructional strategies uptake in introductory chemistry, mathematics, and physics. Brandon then joined the group Marilyne Stains at the University of Virginia before starting his independent career at the University of Georgia. Education Education: Ph.D. in Chemistry (Chemistry Education Research), University of South Florida (2022) M.S. in Chemistry (Inorganic), Georgia Institute of Technology (2019) M.S. in Chemistry (Materials), University of Michigan (2016) B.S. in Chemistry, University of Michigan (2015) Research Research Areas: Chemical Education Selected Publications Selected Publications: Yik, B. J., & Dood, A. J. (2024). ChatGPT explains organic chemistry reaction mechanisms slightly inaccurately with high levels of explanation sophistication. Journal of Chemical Education, 101(5), 1836–1846. DOI: 10.1021/acs.jchemed.4c00235. Machost, H., Kable, E. A., Mitchell-Jones, J. K., Yik, B. J., & Stains, M. (2024). Characterization of physics and astronomy assistant professors’ reflections on their teaching: can they promote engagement in instructional change? Disciplinary and Interdisciplinary Science Education Research, 6:14. DOI: 10.1186/s43031-024-00105-9. Crowder, C. C., Yik, B. J., Frost, S. J. H., Cruz-Ramírez de Arellano, D., & Raker, J. R. (2024). Impact of prompt cueing on level of explanation sophistication for organic reaction mechanisms. Journal of Chemical Education, 101(2), 398–410. DOI: 10.1021/acs.jchemed.3c00710. Yik, B. J., Schreurs, D. G., & Raker, J. R. (2023). Implementation of an R Shiny App for instructors: An automated text analysis formative assessment tool for evaluating Lewis acid–base model use. Journal of Chemical Education, 100(8), 3107–3113. DOI: 10.1021/acs.jchemed.3c00400. Couch, B. A., Prevost, L. B., Stains, M., Whitt, B., Marcy, A. E., Apkarian, N., Dancy, M. H., Henderson, C., Johnson, E., Raker, J. R., Yik, B. J., Earl, B., Shadle, S. E., Skvoretz, J., & Ziker, J. P. (2023). Examining whether and how instructional coordination occurs within introductory undergraduate STEM courses. Frontiers in Education, 8:1156781. DOI: 10.3389/feduc.2023.1156781. Frost, S. J. H., Yik, B. J., Dood, A. J., Cruz-Ramírez de Arellano, D., Fields, K. B., & Raker, J. R. (2023). Evaluating electrophile and nucleophile understanding: A large-scale study of learners’ explanations of reaction mechanisms. Chemistry Education Research and Practice, 24(2), 706–722. DOI: 10.1039/D2RP00327A. Yik, B. J., Dood, A. J., Frost, S. J. H., Cruz-Ramírez de Arellano, D., Fields, K. B., & Raker J. R. (2023). Generalized rubric for level of explanation sophistication for nucleophiles in organic chemistry reaction mechanisms. Chemistry Education Research and Practice, 24(1), 263–282. DOI: 10.1039/D2RP00184E. Yik, B. J., Raker, J. R., Apkarian, N., Stains, M., Henderson, C., Dancy, M. H., & Johnson E. (2022). Association of malleable factors with adoption of research-based instructional strategies in introductory chemistry, mathematics, and physics. Frontiers in Education, 7:1016415. DOI: 10.3389/feduc.2022.1016415. Yik, B. J., Raker, J. R., Apkarian, N., Stains, M., Henderson, C., Dancy, M. H., & Johnson E. (2022). Evaluating the impact of malleable factors on percent time lecturing in gateway chemistry mathematics, and physics courses. International Journal of STEM Education, 9:15. DOI: 10.1186/s40594-022-00333-3. Yik, B. J., Dood, A. J., Cruz-Ramírez de Arellano, D., Fields, K. B., & Raker, J. R. (2021). Development of a machine learning-based tool to evaluate correct Lewis acid–base model use in written responses to open-ended formative assessment items. Chemistry Education Research and Practice, 22(4), 866–885. DOI: 10.1039/D1RP00111F. Courses Regularly Taught: CHEM 2211