This dissertation study used multinomial logistic and ordinary least squares regression models to investigate to what extent students' mathematical sense of belonging predicted their participation and persistence in their mathematical college and career coursework. Mathematical participation and persistence were operationally defined as courses completed in high school and postsecondary, undergraduate mathematics college and careers credits. Framed on extant research regarding equitable mathematics of education and various learning theories, this study offers mathematics domain-specific statistical analyses of belongingness based on Mahar and colleagues' (2012) five transdisciplinary themes of belongingness. Data from the High School Longitudinal Study 2009 (HSLS:09) were used for analyses. First, an exploratory factor analysis (EFA) was conducted to create a mathematical belongingness construct. Then resulting mathematical belongingness subscales, a criterion-referenced mathematics assessment, and demographic variables were analyzed to find statistically significant predictors of students' participation and persistence in the high school mathematics pipeline and in postsecondary undergraduate, mathematics credit hours. Predictors varied in statistical significance within and between the generated models. However, one theme of the transdisciplinary belongingness subscale in the EFA, subjectivity/mathematics identity, was the only affective variable which was statistically significant in each model generated. Interpretations of these results suggest this study be used as support for the field to begin empirically defining and refining conceptions of mathematics-specific belongingness. Implications for research and practice with respect to mathematics identity are shared.
| Identifer | oai:union.ndltd.org:ucf.edu/oai:stars.library.ucf.edu:etd2020-2227 |
| Date | 01 January 2022 |
| Creators | Kebreab, Lybrya |
| Publisher | STARS |
| Source Sets | University of Central Florida |
| Language | English |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | Electronic Theses and Dissertations, 2020- |
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