Skip to main content
SpringerLink
Log in

An Investigation of Harmony Public School Students’ College Enrollment and STEM Major Selection Rates and Perceptions of Factors in STEM Major Selection

  • Published:
International Journal of Science and Mathematics Education Aims and scope Submit manuscript

Abstract

The purpose of this study is to compare the college enrollment and STEM college major choice rates of graduates of a STEM-focused charter school system to those of students who graduated from traditional public schools in the state of Texas and the USA for low-income, first-generation, and underrepresented groups. In addition, the factors students perceived as important in their STEM career selection were examined. Participants were Harmony Public Schools (HPS) alumni who graduated between year 2002 and 2016. Data were collected through annual and additional surveys via e-mails and Facebook. Data were analyzed descriptively to answer the research questions. It was found that HPS had significantly higher college enrollment rates in all minority groups including female, African American, Hispanic, and low-SES when compared to public school students in the state of Texas and the USA. The second research question revealed that HPS graduates’ STEM major choice rates were significantly higher than their counterparts in the state of Texas and the USA in all subgroups including female and students of color. Students’ self-interest, teachers, and parents were found to be the top three factors that students thought affected their major choice.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  • American Association of State Colleges and Universities. (2005). Strengthening the science and mathematics pipeline for a better America (Vol. 2, Publication No. 11). Retrieved from http://www.aascu.org/uploadedFiles/AASCU/Content/Root/PolicyAndAdvocacy/PolicyPublications/STEMPipeline.pdf.

  • Andersen, L., & Ward, T. J. (2014). Expectancy-value models for the STEM persistence plans of ninth-grade, high-ability students: A comparison between Black, Hispanic, and White students. Science Education, 98(2), 216–242.

    Google Scholar 

  • Archer, L., Dewitt, J., & Wong, B. (2013). Spheres of influence: What shapes young people’s aspirations at age 12/13 and what are the implications for education policy? Journal of Education Policy, 29(1), 58–85. https://doi.org/10.1080/02680939.2013.790079.

    Article  Google Scholar 

  • Bandura, A. (1986). Social foundations of thought and action: A social cognitive theory. Englewood Cliffs, NJ, US: Prentice-Hall.

    Google Scholar 

  • Banerjee, P. A. (2016). A systematic review of factors linked to poor academic performance of disadvantaged students in science and math in schools. Cogent Education, 3(1), 117844.

    Google Scholar 

  • Barr, D. A., Gonzalez, M. E., & Wanat, S. F. (2008). The leaky pipeline: Factors associated with early decline in interest in premedical studies among underrepresented minority undergraduate students. Academic Medicine, 83(5), 503–511.

    Google Scholar 

  • Benner, A. D., & Mistry, R. S. (2007). Congruence of mother and teacher educational expectations and low-income youth's academic competence. Journal of Educational Psychology, 99(1), 140–153.

    Google Scholar 

  • Bryant, B. K., Zvonkovic, A. M., & Reynolds, P. (2006). Parenting in relation to child and adolescent vocational development. Journal of Vocational Behavior, 69(1), 149–175. https://doi.org/10.1016/j.jvb.2006.02.004.

    Article  Google Scholar 

  • Bureau of Labor Statistics. (2015). College enrollment and work activity of 2015 high school graduates. Retrieved from https://www.bls.gov/news.release/archives/hsgec_04282016.pdf

  • Buxton, C. A. (2001). Modeling science teaching on science practice? Painting a more accurate picture through an ethnographic lab study. Journal of Research in Science Teaching, 38, 387–407.

    Google Scholar 

  • Carnevale, A. P., Smith, N., & Strohl, J. (2013). Recovery: Job growth and education requirements through 2020. Washington, DC: Georgetown University, Center on Education and the Workforce.

    Google Scholar 

  • Chapman, C., Laird, J., Ifill, N., & KewalRamani, A. (2011). Trends in high school dropout and completion rates in the United States: 1972–2009 (NCES 2012–006). Washington, DC: National Center for Education Statistics.

    Google Scholar 

  • Chen, X., & Solder, M. (2013). STEM attrition: College students’ paths into and out of STEM fields (NCES 2014–001). Washington, DC: U.S. Department of Education and National Center for Education Statistics.

    Google Scholar 

  • Dabney, K., Tai, R., Almarode, J., Miller-Friedmann, J., Sonnert, G., Sadler, P., & Hazari, Z. (2011). Out- of-school time science actvities and their association with career interest in STEM. International Journal of Science Education, Part B: Communication and Public Engagement, 2(1), 63–79. https://doi.org/10.1080/21548455.2011.629455.

    Article  Google Scholar 

  • Dawes, L. A., Long, S., Whiteford, C., & Richardson, K. (2015). Why are students choosing STEM and when do they make their choice? In A. Oo & A. Patel (Eds.), Proceedings of 26th annual conference of the Australasian Association for Engineering. Vic: School of Engineering, Deakin University, Geelong.

    Google Scholar 

  • Desilver, D. (2014). College enrollment among low-income students still trails richer groups. Retrieved from http://www.pewresearch.org/fact-tank/2014/01/15/college-enrollment-among-low-income-students-still-trails-richer-groups/

  • Drew, D. E. (2011). Stem the tide: Reforming science, technology, engineering, and math education in America. Baltimore, MD: Johns Hopkins University Press.

    Google Scholar 

  • Eccles, J. S., & Wigfield, A. (2002). Motivational beliefs, values, and goals. Annual Review of Psychology, 53(1), 109–132.

    Google Scholar 

  • Eisenhart, M., Weis, L., Allen, C. D., Cipollone, K., Stich, A., & Dominguez, R. (2015). High school opportunities for STEM: Comparing inclusive STEM-focused and comprehensive high schools in two US cities. Journal of Research in Science Teaching, 52(6), 763–789.

    Google Scholar 

  • Gottfried, M. A., & Williams, D. (2013). STEM club participation and STEM schooling outcomes. Education Policy Analysis Archives, 21(79), 1–27.

    Google Scholar 

  • Ingels, S. J., Pratt, D. J., Herget, D. R., Dever, J. A., Fritch, L. B., Ottem, R., ... & Leinwand, S. (2013). High School Longitudinal Study of 2009 (HSLS: 09) Base Year to First Follow-Up Data File Documentation. Appendixes. NCES 2014–361. National Center for Education Statistics.

  • Ingels, S. J., Pratt, D. J., Herget, D. R., Bryan, M., Fritch, L. B., Ottem, R., et al. (2015). The High School Longitudinal Study of 2009 (HSLS: 09): 2013 update and high school transcript (NCES 2015-036). Washington: National Center for Education Statistics.

  • Kena, G., Musu-Gillette, L., Robinson, J., Wang, X., Rathbun, A., Zhang, J., Wilkinson-Flicker, S., Barmer, A., & Dunlop Velez, E. (2015). The condition of education 2015 (NCES 2015–144) .Washington, DC: U.S. Department of Education, National Center for Education Statistics. Retrieved from http://nces.ed.gov/pubsearch.

  • Lee, J., & Shute, V. (2010). Personal and social-contextual factors in K-12 academic performance: An integrative perspective on student learning. Educational Psychologist, 45, 185–202.

    Google Scholar 

  • Lee, S. W., Min, S., & Mamerow, G. P. (2015). Pygmalion in the classroom and the home: Expectation’s role in the pipeline to STEMM. Teachers College Record, 117(9), 1–36.

    Google Scholar 

  • Lent, R. W., & Brown, S. D. (1996). Social cognitive approach to career development: An overview. The Career Development Quarterly, 44(4), 310–321.

    Google Scholar 

  • Lent, R. W., Brown, S. D., & Hackett, G. (1994). Toward a unifying social cognitive theory of career and academic interest, choice, and performance. Journal of Vocational Behavior, 45(1), 79–122.

    Google Scholar 

  • Lent, R. W., & Brown, S. D. (2013). Social cognitive model of career self-management: Toward a unifying view of adaptive career behavior across the life span. Journal of Counseling Psychology, 60(4), 557–568.

    Google Scholar 

  • Li, Q., Swaminathan, H., & Tang, J. (2009). Development of a classification system for engineering student characteristics affecting college enrollment and retention. Journal of Engineering Education, 98(4), 361–376.

    Google Scholar 

  • Lynch, S. J., Burton, E. P., Behrend, T., House, A., Ford, M., Spillane, N., et al. (2017). Understanding inclusive STEM high schools as opportunity structures for underrepresned students: Critical components. Journal of Research in Science Teaching, 55(5), 712–748.

    Google Scholar 

  • Lynch, S. J., Burton, E. P., Behrend, T., House, A., Ford, M., Spillane, N., et al. (2018). Understanding inclusive STEM high schools as opportunity structures for underrepresented students: Critical components. Journal of Research in Science Teaching, 55(5), 712–748.

    Google Scholar 

  • Maltese, A. V., & Cooper, C. S. (2017). STEM pathways: Do men and women differ in why they enter and exit? AERA Open, 3(3), 11–16.

    Google Scholar 

  • Maltese, A. V., & Tai, R. H. (2011). Pipeline persistence: Examining the association of educational experiences with earned degrees in STEM among US students. Science Education, 95(5), 877–907.

    Google Scholar 

  • Means, B., Wang, H., Young, V., Peters, V., & Lynch, S. J. (2016). STEM-focused high schools as a strategy for enhancing readiness for postsecondary STEM programs. Journal of Research in Science Teaching, 53(5), 709–736. https://doi.org/10.1002/tea.21313.

    Article  Google Scholar 

  • Means, B., Wang, H., Wei, X., Lynch, S., Peters, V., Young, V., & Allen, C. (2017). Expanding STEM opportunities through inclusive STEM-focused high schools. Science Education, 101(5), 681–715.

    Google Scholar 

  • National Assessment of Educational Progress. (2015). National results overview. Retrieved from https://www.nationsreportcard.gov/reading_math_2015/#reading?grade=4

  • National Research Council. (2009). Learning science in informal environments: People, places, and pursuits. Retrieved from http://www.nap.edu/catalog.php?record_ id=12190.

  • National Science Board. (2016). Science and engineering indicators. Retrieved from https://www.nsf.gov/nsb/publications/2016/nsb20161.pdf

  • National Science Board. (2018). Science and engineering indicators 2018 (NSB-2018-1). Alexandria, VA: National Science Foundation. Available at https://www.nsf.gov/statistics/indicators/.

    Google Scholar 

  • National Science Foundation, National Center for Science and Engineering Statistics. (2017). Bachelor’s degrees, by sex and field: 2004–14. In In women, minorities, and persons with disabilities in science and engineering: 2017 (special report NSF 17-310). Arlington, VA: Author. Retrieved from https://www.nsf.gov/statistics/2017/nsf17310/static/data/tab5-1.pdf.

  • National Science Teacher Association. (1999). National Science Teacher Association position statement: Informal science education. Retrieved from http://www.nsta.org/about/positions/informal.aspx.

  • Noonan, R. (2017). STEM jobs: 2017 update (ESA issue brief# 02-17). Washington, DC: US Department of Commerce, Economics and Statistics Administration, Office of the Chief Economist.

    Google Scholar 

  • Pham, C., & Keenan, T. (2011). Counseling and college matriculation: Does the availability of counseing affect college-going decisions among highly qualified first-generation college-bound high school graduates? Journal of Applied Economics and Business Research, 1, 12–24.

    Google Scholar 

  • President’s Council of Advisors on Science and Technology. (2012). Engage to Excel: Producing one million additional college graduates with degrees in science, technology, engineering, and mathematics. Retrieved from https://obamawhitehouse.archives.gov/sites/default/files/microsites/ostp/pcast-engage-to-excel-final_2-25-12.pdf

  • Ricks, M. M. (2006). A study of the impact of an informal science education program on middle school students’ science knowledge, science attitude, STEM high school and college course selections, and career decisions (Unpublished doctoral dissertation). The University of Texas at Austin, TX.

  • Rivkin, S., Hanushek, E., & Kain, J. (2005). Teacher, schools, and academic achievement. Econometrica, 73, 417–458.

    Google Scholar 

  • Rodriguez, E., Rhodes, K., & Aguirre, G. (2015). Intervention for high school Latino students in preparing for college: Steps for consideration. Journal of Hispanic Higher Education, 14(3), 2007–2222.

    Google Scholar 

  • Sahin, A. (2013). STEM clubs and science fair competitions: Effects on post-secondary matriculation. Journal of STEM Education: Innovations and Research, 14(1), 7–13.

    Google Scholar 

  • Sahin, A., & Top, N. (2015). STEM students on the stage (sos): Promoting student voice and choice in STEM education through an interdisciplinary, standards-focused project-based learning approach. Journal of STEM Education: Innovation and Research., 16(3), 24–33.

    Google Scholar 

  • Sahin, A., Ayar, M. C., & Adiguzel, T. (2014). STEM-related after-school program activities and associated outcomes on student learning. Educational Sciences: Theory & Practice, 14(1), 13–26.

    Google Scholar 

  • Sahin, A., Oren, M., Willson, V., Hubert, T., & Capraro, R. M. (2015). Longitudinal analysis of T-STEM academies: How do Texas inclusive STEM academies (T-STEM) perform in mathematics, science, and reading? International Online Journal of Educational Sciences, 7(4), 11–21.

    Google Scholar 

  • Sahin, A., Ekmekci, A., & Waxman, H. (2017a). Characteristics of students who majored in STEM fields. International Journal of Science Education, 39(11), 1549–1572. https://doi.org/10.1080/09500693.2017.1341067.

    Article  Google Scholar 

  • Sahin, A., Ekmekci, A., & Waxman, H. (2017b). Collective effects of individual, behavioral, and contextual factors on high school students’ future STEM career plans. International Journal of Science and Mathematics Education, 1–21. https://doi.org/10.1007/s10763-017-9847-x.

  • Sass, T. R. (2015). Understanding the STEM pipeline (CALDER #125). Washington, DC: American Institutes for Research.

    Google Scholar 

  • Schiefele, U., Krapp, A., & Winteler, A. (1992). Interest as a predictor of academic achievement: A meta-analysis of research. In K. A. Renninger, S. Hidi, & A. Krapp (Eds.), The role of interest in learning and development (pp. 183–212). Hillsdale, NJ: Erlbaum.

    Google Scholar 

  • Schoon, I., Martin, P., & Ross, A. (2007). Career transitions in times of social change. His and her story. Journal of Vocational Behavior, 70(1), 78–96. https://doi.org/10.1016/j.jvb.2006.04.009.

    Article  Google Scholar 

  • Seymour, E. (2001). Tracking the processes of change in U.S. undergraduate education in science, mathematics, engineering, and technology. Science Education, 86(1), 79–105.

    Google Scholar 

  • Simpkins, S. D., Davis-Kean, P. E., & Eccles, J. S. (2006). Math and science motivation: A longitudinal examination of the links between choices and beliefs. Developmental Psychology, 42(1), 70–83.

    Google Scholar 

  • Tai, R. H., Liu, C. Q., Maltese, A. V., & Fan, X. (2006). Planning early for careers in science. Science Education, 312, 1143–1144.

    Google Scholar 

  • Texas Higher Education Coordinating Board. (2016). Tracking postsecondary outcomes dashboard: Texas high school graduates. Retrieved from http://reports.thecb.state.tx.us/approot/hs_college/hs_college_main_launch.htm

  • The College Board. (2014). Program research: AP data. Retrieved from http://research.collegeboard.org/programs/ap/data/archived/ap-2014

  • U.S. Census Bureau. (2012) Educational attainment in the United States: 2012 - Detailed tables Retrieved from https://www.census.gov/hhes/socdemo/education/data/cps/2012/tables.html

  • U.S. Department of Education, Office for Civil Rights (2014). Civil rights data collection. Data Snapshot: College and career readiness. Retrieved from https://www2.ed.gov/about/offices/list/ocr/docs/crdc-college-and-career-readiness-snapshot.pdf

  • U.S. Department of Education, Office of Innovation and Improvement. (2016). STEM 2026: A vision for innovation in STEM education. Washington, DC: Author.

    Google Scholar 

  • U.S. Department of Labor. (2007). The STEM workforce challenge: the role of the public workforce system in a national solution for a competitive science, technology, engineering, and mathematics (STEM) workforce. Retrieved from www.dol.gov

  • Vaval, L., Bowers, A. J., & Snodgrass Rangel, V. (2019). Identifying a typology of high schools based on their orientation toward STEM: A latent class analysis of HSLS: 09. Science Education. 103(5), 1151–1175.

  • Vedder-Weiss, D., & Fortus, D. (2013). School, teacher, peers, and parents’ goals emphases and adolescents’ motivation to learn science in and out of school. Journal of Research in Science Teaching, 50, 952–988.

    Google Scholar 

  • Wang, X. (2013). Why students choose STEM majors: Motivation, high school learning, and postsecondary context of support. American Educational Research Journal, 50(5), 1081–1121.

    Google Scholar 

  • Wang, M. T., & Degol, J. L. (2017). Gender gap in science, technology, engineering, and mathematics (STEM): Current knowledge, implications for practice, policy, and future directions. Educational Psychology Review, 29, 119–140.

    Google Scholar 

  • Weinstein, L., & Savitz-Romer, M. (2009). Planning for opportunity: Applying organizational and social capital theories to promote college going cultures. Educational Planning, 18(2), 1–11.

    Google Scholar 

  • Weis, L., Eisenhart, M., Cipollone, K., Stich, A. E., Nikischer, A. B., Hanson, J., et al. (2015). In the guise of STEM education reform: Opportunity structures and outcomes in inclusive STEM-focused high schools. American Educational Research Journal, 52(6), 1024–1059.

    Google Scholar 

  • White, J. L., & Massiha, G. H. (2016). The retention of women in science, technology, engineering, and mathematics: A framework for persistence. International Journal of Evaluation and Research in Education, 5(1), 1–8.

    Google Scholar 

  • White, J. L., Altschuld, J. W., & Lee, Y. F. (2006). Persistence of interest in science, technology, engineering, and mathematics: A minority retention study. Journal of Women and Minorities in Science and Engineering, 12(1), 47–64.

    Google Scholar 

  • Yu, S. L., Corkin, D. M., & Martin, J. P. (2017). STEM motivation and persistence among underrepresented minority students: A social cognitive perspective. In J. T. DeCuir-Gunby & P. A. Schutz (Eds.), Race and ethnicity in the study of motivation in education (pp. 67–81). New York, NY: Taylor & Francis.

    Google Scholar 

  • Zweben, S., & Bizot, B. (2014). 2013 Taulbee survey: Second consecutive year of record doctoral degree production; continued strong undergraduate CS enrollment. Computing Research News, 26(5), 10–55.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Academics Department, Harmony Public Schools, 9321 W Sam Houston Pkwy S, Houston, TX, 77099, USA

    Alpaslan Sahin

  2. College of Education and Human Development, Texas A & M University, 361 Harrington Tower, College Station, TX, 77843-4232, USA

    Hersh C. Waxman

  3. Houston, TX, 77099, USA

    Edward Demirci

  4. Department of Educational Leadership and Policy Studies, College of Education, University of Houston, Farish Hall, 112G, Houston, TX, USA

    Virginia Snodgrass Rangel

Authors
  1. Alpaslan Sahin
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Hersh C. Waxman
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Edward Demirci
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Virginia Snodgrass Rangel
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Alpaslan Sahin.

Ethics declarations

All the authors have approved the manuscript submission. The content of the manuscript has not been published before.

Conflict of Interest

The authors declare that there is no conflict of interest

Appendix

Appendix

figure afigure afigure afigure afigure a

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Sahin, A., Waxman, H.C., Demirci, E. et al. An Investigation of Harmony Public School Students’ College Enrollment and STEM Major Selection Rates and Perceptions of Factors in STEM Major Selection. Int J of Sci and Math Educ 18, 1249–1269 (2020). https://doi.org/10.1007/s10763-019-10017-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10763-019-10017-0

Keywords

Search

Navigation