Why Students Choose STEM Majors: Motivation, High School Learning, and Postsecondary Context of Support

Issue/Topic: STEM; Postsecondary Success; Postsecondary Participation
Author(s): Wang, Xueli
Organization(s): University of Wisconsin-Madison
Publication: American Educational Research Journal
Published On: 5/21/2013

By 2018, nine of the top 10 fastest growing occupations will require significant training in math or science. However, the shortage of students pursuing STEM disciplines creates a need for postsecondary institutions to increase the number of students who enter STEM majors. This study focuses on the full STEM pipeline, especially factors that affect math interest and ability before enrollment in a major.

To study how several high school factors (i.e., math and science exposure, achievement and motivation) affect intent to pursue and enter STEM fields of study. To examine the effect of initial postsecondary education experiences, such as academic interaction, receipt of financial aid, and remediation on STEM intent and enrollment. To explore differences in intent and enrollment by gender, ethnicity, and socioeconomic status.


High School Variables

  • All three 12th-grade variables (i.e., self-efficacy, course-taking, and math achievement) had positive and statistically significant effects on students' intent to pursue STEM majors.
  • The effect of exposure to math and science on STEM intent was positive and statistically significant across all subgroups, but the smallest effect was among underrepresented minorities.
  • Not surprisingly, 12th grade variables were positively and strongly correlated to the same variables measured in the 10th grade year.

Postsecondary Factors

  • Choosing a STEM major was positively associated with intent to major in STEM, college readiness in math and science, receiving financial aid, and expecting to earn a graduate degree.
  • Receiving remediation and being enrolled full-time did not show any influence on STEM entrance.
  • None of these effects differs significantly across racial, gender, and socioeconomic groups.

Policy Implications/Recommendations:

Math and Science in High School

  • In order to boost high school students' interest in pursuing STEM fields, an earlier introduction and exposure to math and science courses could be an effective method.
  • A stronger alignment between high school offerings and academic expectations at the college level represents a promising step toward promoting greater student interest and entrance into STEM fields.

Importance of Self-Belief

  • While exposure to advanced math and science course options can spur interest, this study highlights the importance of cultivating students' positive attitudes toward math.
  • Improving female students' math self-efficacy may also help cultivate stronger interest in pursuing STEM among female students with equivalent math achievement to their male peers.
  • Addressing Postsecondary Barriers

    • Choosing a STEM major is largely dependent on motivation and belief. Since postsecondary institutions cannot reverse negative self-beliefs completely, policymakers should think about the supply side of the STEM pipeline: that is, the students in the K-12 system that would benefit from pursuing a STEM career.
    • Colleges and universities can employ outreach programs to increase STEM interest among women and students of color.

    The study can be accessed by accessing this link: http://aer.sagepub.com/content/50/5/1081.

    Research Design:
    Use of theoretical model that combines social cognitive career theory and elements of the academic literature related to college students' academic choices and outcomes. Model elements include 1) grade 12 math achievement; 2) exposure to math and science courses in high school; 3) math self-efficacy beliefs; and 4) postsecondary supports and barriers (e.g., academic interaction, financial aid, students' college readiness status, graduate degree expectations, and attendance status).

    Student sample from Education Longitudinal Study of 2002. Year 2002 sample contained high school sophomores. Follow ups in 2004 and 2006. Subgroup created (n=6,300) based on recent graduates that entered four-year institution.

    Year data is from:
    2002, 2004, and 2006


    Data Collection and Analysis:
    Descriptive analysis, followed by a two-step modeling approach. An analysis was performed to measure the fit of the theoretical framework with the data. Once the fit was confirmed, the researcher subjected the data to five regression analyses. The final analysis studied whether the theoretical framework was equivalent across subgroups.


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