Describing and Mapping the Interactions between Student Affective Factors Related to Persistence in Science, Physics, and Engineering

Abstract

This dissertation explores how students' beliefs and attitudes interact with their identities as physics people, motivated by calls to increase participation in science, technology, engineering, and mathematics (STEM) careers. This work combines several theoretical frameworks, including Identity theory, Future Time Perspective theory, and other personality traits to investigate associations between these factors. An enriched understanding of how these attitudinal factors are associated with each other extends prior models of identity and link theoretical frameworks used in psychological and educational research. The research uses a series of quantitative and qualitative methodologies, including linear and logistic regression analysis, thematic interview analysis, and an innovative analytic technique adapted for use with student educational data for the first time: topological data analysis via the Mapper algorithm. Engineering students were surveyed in their introductory engineering courses.

Engineering Women’s Attitudes and Goals in Choosing Disciplines with Above and Below Average Female Representation

“…Our findings suggest that a physics identity is more salient among women in engineering disciplines with below average female representation (β = 0.17, p < 0.001), whereas women in disciplines with above average female representation i.e., industrial engineering, chemical engineering, biological and agricultural engineering, biomedical engineering, environmental engineering, were less likely to identify as a physics person (β = −0.09, p < 0.05). These results are consistent with related work, from a sample of over 2000 male and female first-year engineering students, that found physics identity to be a positive, significant predictor of interest in aerospace engineering, mechanical engineering, electrical engineering and construction management engineering, and negatively related to biomedical engineering (Doyle 2017). …”

Using Topological Data Analysis in Social Science Research: Unpacking Decisions and Opportunities for a New Method

“…Most students in the study (n = 3,233) had similar enough attitudes to be connected with the resulting mapping. This finding is different from previous work using TDA with four U.S. institutions [25]. We hypothesize that the larger representation of different institution types, sizes, and geographic locations may be one reason in identifying a broader common set of important attitudes, beliefs, and mindsets for students entering engineering.…”

“…In general, a higher k will result in more a connected map and a smaller ungrouped set of points, and a lower k will result in a less connected map with a larger ungrouped set of points. There is not a "best" value for k, and the impact of this parameter on the resulting filters should be visualized for each dataset [12], [25]. Similar to other statistical techniques, like cluster analysis, the decision of how many clusters to choose or in exploratory factor analysis, the number of factors to extract, this method is often iterative and determined through graphical means.…”

“…Based on our experience with engineering education data [25], the number of filter slices, n, should be large enough to result in enough points within each filter slice to be able to conduct future statistical analysis. Using the central limit theorem, we recommend at minimum of 30, but encourage larger slices for analyses by additional variables.…”

New Epistemological Perspectives on Quantitative Methods: An Example Using Topological Data Analysis

“…We would also like to thank the STRIDE (Shaping Transformative Research on Identity and Diversity in Engineering) research group for their assistance in data collection and review of findings for this project. Specifically, the authors would like to thank Dr. Jacqueline Doyle for her work in developing the Mapper algorithm (Doyle, 2017) used to conduct the TDA analysis and her consultation in data analysis. We would also like to thank Dr. Adam Kirn for his conversations about person-centered analyses and Dr. Elliot Douglas for his discussion of epistemic framings in research with the first author.…”