Document Type
Conference Proceeding
Publication Date
6-29-2026
Abstract
Inequities in mathematics-intensive engineering pathways persist in part because "being mathematical" is socially recognized as much as it is personally meaningful. Guided by the Social Theory of Numeracy, this study validates a two-factor model of mathematics identity, distinguishing between identity centrality (the integration of mathematics into one's self-concept) and peer-normative typicality (the perceived alignment with academic peer pathways that signal legitimacy and belonging). Engineering degree selection was examined as a focal mathematics-intensive outcome. Using the High School Longitudinal Study of 2009 (HSLS:09), we estimated survey-weighted confirmatory and structural models with Balanced Repeated Replication (200 BRR replicates; Fay = 0.5) on a nationally representative analytic sample of 12,948 students. Discriminant validity in the full sample rejected a one-factor solution (Wald F(1,199) = 279.45, p < .001), with moderate factor covariance ϕ = 0.22 (SE = 0.046, p < .001). Structural models revealed that centrality was the primary direct predictor of selecting an engineering degree. In contrast, peer-normative typicality was more strongly associated with contextual factors, such as peer norms and school belonging, operating as an ecological signal of legitimacy. Results indicate that stratification in engineering pathways emerges when students develop high centrality in contexts that do not confer peer-normative legitimacy. In the fully adjusted specification, peer-normative typicality does not serve as a direct predictor; instead, it operates as a modifiable ecological condition that shapes whether strong mathematical self-concepts translate into engineering degree pathways.
Recommended Citation
D. D. Havard, L. Ho and A. Quirós-Arauz, "Modeling Mathematics Identity in Engineering Pathways: Centrality, Typicality, and Persistence in STEM," 2026 IEEE Global Engineering Education Conference (EDUCON), Cairo, Egypt, 2026, pp. 1-10, https://doi.org/10.1109/EDUCON67543.2026.11574326.
Copyright
© 2026 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Included in
Engineering Education Commons, Higher Education Commons, Science and Mathematics Education Commons
Comments
This is a pre-copy-editing, author-produced PDF of an article accepted for publication in 2026 IEEE Global Engineering Education Conference (EDUCON). This article may not exactly replicate the final published version. The definitive publisher-authenticated version is available online at https://doi.org/10.1109/EDUCON67543.2026.11574326.