Why Students Choose STEM Majors: Motivation, High School Learning, and Postsecondary Context of Support
Abstract
This study draws upon social cognitive career theory and higher education literature to test a conceptual framework for understanding the entrance into science, technology, engineering, and mathematics (STEM) majors by recent high school graduates attending 4-year institutions. Results suggest that choosing a STEM major is directly influenced by intent to major in STEM, high school math achievement, and initial postsecondary experiences, such as academic interaction and financial aid receipt. Exerting the largest impact on STEM entrance, intent to major in STEM is directly affected by 12th-grade math achievement, exposure to math and science courses, and math self-efficacy beliefs—all three subject to the influence of early achievement in and attitudes toward math. Multiple-group structural equation modeling analyses indicated heterogeneous effects of math achievement and exposure to math and science across racial groups, with their positive impact on STEM intent accruing most to White students and least to underrepresented minority students.
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Perceived barriers and motivators are contextual factors that influence underrepresented students' pathways into the health profession and the relationship between health profession career interests and choices.
“Perceived barriers and motivators are contextual factors that influence underrepresented students’ pathways into the health profession and the relationship between health profession career interests and choices.36”
From Perceived Success After Participation in the Summer Health Professions Education Program (2024) · cites this paper as [36]
Cites
Reporting Structural Equation Modeling and Confirmatory Factor Analysis Results: A Review
Schreiber, J. B., Nora, A., Stage, F. K., Barlow, E. A., & King, J. (2006) · The Journal of Educational Research · doi:10.3200/joer.99.6.323-338 · Cited by 6,937 · see citing works
A Beginner's Guide to Structural Equation Modeling: Fourth Edition
Schumacker, R. E., & Lomax, R. G. (2015) · doi:10.4324/9781315749105 · Cited by 6,604 · see citing works
Contextual supports and barriers to career choice: A social cognitive analysis.
Lent, R. W., Brown, S. D., & Hackett, G. (2000) · Journal of Counseling Psychology · doi:10.1037/0022-0167.47.1.36 · Cited by 2,285 · see citing works
Faculty Student Interaction at the Community College: A Focus on Students of Color
Chang, J. C. (2005) · Research in Higher Education · doi:10.1007/s11162-004-6225-7 · Cited by 148 · see citing works
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Anatomy of an Enduring Gender Gap: The Evolution of Women’s Participation in Computer Science
Sax, L. J., Lehman, K. J., Jacobs, J. A., Kanny, M. A., Lim, G., Monje-Paulson, L., & Zimmerman, H. B. (2017) · The Journal of Higher Education · doi:10.1080/00221546.2016.1257306 · Cited by 261 · see citing works
Grigg, S., Perera, H. N., McIlveen, P., & Svetleff, Z. (2018) · Contemporary Educational Psychology · doi:10.1016/j.cedpsych.2018.01.007 · Cited by 180 · see citing works
Hagger, M. S., Sultan, S., Hardcastle, S. J., & Chatzisarantis, N. L. D. (2015) · Contemporary Educational Psychology · doi:10.1016/j.cedpsych.2014.12.002 · Cited by 171 · see citing works
Regan, E., & DeWitt, J. (2015) · Understanding Student Participation and Choice in Science and Technology Education · doi:10.1007/978-94-007-7793-4_5 · Cited by 122 · see citing works
Becoming a Scientist—Career Choice Characteristics
Sasson, I. (2021) · International Journal of Science and Mathematics Education · doi:10.1007/s10763-020-10059-9 · Cited by 33 · see citing works
Oliver, M. C., Woods-McConney, A., Maor, D., & McConney, A. (2017) · International Journal of STEM Education · doi:10.1186/s40594-017-0060-9 · Cited by 20 · see citing works
Wellberg, S. (2025) · Journal of Mathematics Teacher Education · doi:10.1007/s10857-024-09640-8 · Cited by 10 · see citing works
Perceived Success After Participation in the Summer Health Professions Education Program
Xirau-Probert, P., Lai, T., Black, E., Fanfan, D., Blue, A., Rush, C. C., Powers, R., & Stacciarini, J.-M. R. (2024) · JAMA Network Open · doi:10.1001/jamanetworkopen.2023.52440 · Cited by 8 · see citing works
Dougherty, K. J. (2024) · Teachers College Record: The Voice of Scholarship in Education · doi:10.1177/01614681241240287 · Cited by 6 · see citing works
Wladis, C., Sencindiver, B., Hachey, A. C., Myszkowski, N., Offenholley, K., Samuels, J., & Aly, G. (2026) · AERA Open · doi:10.1177/23328584261419497 · Cited by 1 · see citing works
- Authors
- Wang, X.
- Affiliations
- University of Wisconsin–Madison (US)
- Corresponding author
- Wang, Xueli
- ORCID
- Wang, Xueli
- Venue
- American Educational Research Journal
- Published
- 2013-10
- Volume
- 50
- Issue
- 5
- Pages
- 1081-1121
- ISSN
- 0002-8312 (ISSN-L)
- 1935-1011
- OpenAlex
- W2065048560
- Cited by
- 896
- References
- 4
- Citations by year
- 2026: 56 · 2025: 59 · 2024: 97 · 2023: 70 · 2022: 94 · 2021: 88 · 2020: 91 · 2019: 96 · 2018: 60 · 2017: 76 · 2016: 56 · 2015: 34 · 2014: 15 · 2013: 2
- FWCI
- 87.47
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- American Educational Research Journal (publishedVersion)
- Topics
- Social Sciences › Safety Research › Career Development and Diversity
- Social Sciences › Education › Higher Education Research Studies
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Education, Achievement, and Giftedness
- Keywords
- STEM major choice, social cognitive career theory, mathematics self-efficacy, financial aid, racial differences, underrepresented minority students, university students
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