Her research focuses what factors influence diverse students to choose engineering and stay in engineering through their careers and how different experiences within the practice and culture of engineering foster or hinder belongingness and identity development. Dr. Godwin graduated from Clemson University with a B.S. in Chemical Engineering and Ph.D. in Engineering and Science Education. Her research earned her a National Science Foundation CAREER Award focused on characterizing latent diversity, which includes diverse attitudes, mindsets, and approaches to learning, to understand engineering students' identity development. She is the recipient of a 2014 American Society for Engineering Education (ASEE) Educational Research and Methods Division Apprentice Faculty Grant. She has also been recognized for the synergy of research and teaching as an invited participant of the 2016 National Academy of Engineering Frontiers of Engineering Education Symposium and 2016 New Faculty Fellow for the Frontiers in Engineering Education Annual Conference. She also was an NSF Graduate Research Fellow for her work on female empowerment in engineering which won the National Association for Research in Science Teaching 2015 Outstanding Doctoral Research Award.

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Her research focuses what factors influence diverse students to choose engineering and stay in engineering through their careers and how different experiences within the practice and culture of engineering foster or hinder belongingness and identity development. Dr. Godwin graduated from Clemson University with a B.S. in Chemical Engineering and Ph.D. in Engineering and Science Education. Her research earned her a National Science Foundation CAREER Award focused on characterizing latent diversity, which includes diverse attitudes, mindsets, and approaches to learning, to understand engineering students' identity development. She has won several awards for her research including

Background: Noncognitive and affective (NCA) factors (e.g., belonging, engineering identity, motivation, mindset, personality, etc.) are important to undergraduate student success. However, few studies have considered how these factors coexist and act in concert.Purpose/Hypothesis: We hypothesize that students cluster into several distinct collections of NCA factors and that identifying and considering the factors together may inform student support programs and engineering education. Design/Method: We measured 28 NCA factors using a survey instrument with strong validity evidence. We gathered responses from 2339 engineering undergraduates at 17 U.S. institutions and used Gaussian mixture modeling (GMM) to group respondents into clusters.Results: We found four distinct profiles of students in our data and a set of unclustered students with the NCA factor patterns varying substantially by cluster. Correlations of cluster membership to self-reported incoming academic performance measures were not strong, suggesting that students' NCA factors rather than traditionally used cognitive measures may better distinguish among students in engineering programs. Conclusions: GMM is a powerful technique for person-centered clustering of high-dimensional datasets. The four distinct clusters of students discovered in this research illustrate the diversity of engineering students' NCA profiles. The NCA factor patterns within the clusters provide new insights on how these factors may function together and provide opportunities to intervene on multiple factors simultaneously, potentially resulting in more comprehensive and effective interventions. This research leads to future work on both student success modeling and student affairs-academic partnerships to understand and promote holistic student success.