2018 - The Role of High School Racial Composition and Opportunities to Learn in Students’ STEM College Participation

Attribution: Bottia, Martha C., Stearns, Elizabeth, Mickelson, Roslyn A., Moller, Stephanie, & Giersch, Jason
Researchers: Elizabeth StearnsJason GierschMartha C. BottiaRoslyn A. MickelsonStephanie Moller
University Affiliation: University of North Carolina at Charlotte
Email: mbottia@uncc.edu
Research Question:
1) Do the rates of STEM declaration and graduation vary between high schools? 2) Do opportunities to learn science and mathematics vary depending on high schools' racial composition? 3) What is the relationship between high school racial composition, opportunities to learn available at high schools, and students' STEM participation in college? 4) Do these relationships vary by racial/ethnic groups?
Published: Yes
Journal Name or Institutional Affiliation: Journal of Research in Science Teaching
Journal Entry: Vol.55(3), pp.446-476
Year: 2017
Findings:

– Attending segregated-White schools is negatively associated with declaring a STEM major and with graduating with a STEM major irrespective of students’ own race.
– With all other individual, school, and family factors held constant, exposure to broader curricular offerings available in segregated White high schools appears to be a countervailing force that reduces the likelihood of the pursuit of STEM in college, at least for White adolescents.
– While attending a whiter school has a negative relationship with White students’ short term STEM participation (lower declaration rates) but not with White students’ long term STEM outcomes (graduation), attending a White school does not have a short term association with Black students’ declaration of STEM as a major, but it does have a long term negative association with Black students’ odds of graduation with a STEM major.
– A high school’s Title 1 status, library resources, number of high quality teachers, availability of advanced STEM courses, and availability of non-STEM courses showed no statistically significant relationship with declaring or completing STEM majors, regardless of student race.

* High school composition and it’s impact on majoring and graduating in STEM.

Scholarship Types: Journal Article Reporting Empirical ResearchKeywords: CollegeOpportunity StructureRaceRacial CompositionSTEMRegions: SouthMethodologies: QuantitativeResearch Designs: Secondary Survey DataAnalysis Methods: Cross-Classified HGLM Sampling Frame:High school to college students
Sampling Types: Population of a StateAnalysis Units: SchoolStudentData Types: Quantitative-Longitudinal
Data Description:
  • The authors utilize the concept of bounded rationality. Bounded rationality refers to behavior that is rational but limited by the cognitive constraints and information available for decision making.
  • North Carolina Roots of STEM Success project, which contains longitudinal information on the academic performance and scholastic experiences among 2004 North Carolina public school graduates who also matriculated to one of the 16 campuses of the University of North Carolina system. The analytic sample contains 12,200 White, Black, 8, Latino and Native American college-bound students who attended one of 330 high schools in North Carolina.
  • Dvs: The first one is whether students declared a STEM major or not in college. The second is if students’ actually graduated with a STEM major or not.
  • Ivs: The key independent variable measures school racial/ethnic composition indicated by the percent White students within each high school during the years 2000 and 2003.
  • The authors also included several measures to control for socioeconomic status of the student body. One measure of school’s SES is the school average parental educational attainment, which likely indirectly captures differences in parents’ income and values about education. Another indicator of school SES context is whether the school is designated as a Title 1 school (has over 40% of their students receiving free/reduced lunch) or not.
  • To test the importance of a school’s OTL in students’ processes of selection of major, the authors include measures of quality of math and science courses (number of AP/IB math and science courses per student at a school), quality of teachers (percent of courses taught by high quality teachers), educational resources (books per student, students per instructional computers), and exposure to non-STEM subjects (proportion of non-STEM courses available at school given all course courses offered) at the school level.
Theoretical Framework:
Relevance:Factors Related to STEM Readiness
Archives: K-16 STEM Abstracts