science, technology, engineering, and mathematics (stem) pathways: high school science and math...
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This article was downloaded by: [Universidad Autonoma de Barcelona]On: 16 October 2014, At: 05:04Publisher: RoutledgeInforma Ltd Registered in England and Wales Registered Number: 1072954Registered office: Mortimer House, 37-41 Mortimer Street, London W1T 3JH,UK
Journal of Education forStudents Placed at Risk(JESPAR)Publication details, including instructions forauthors and subscription information:http://www.tandfonline.com/loi/hjsp20
Science, Technology,Engineering, and Mathematics(STEM) Pathways: High SchoolScience and Math Courseworkand Postsecondary DegreeAttainmentWill Tyson a , Reginald Lee b , Kathryn M. Borman b &Mary Ann Hanson ca Department of Sociology , University of SouthFloridab Department of Anthropology , University of SouthFloridac Center for Career and Community ResearchPublished online: 05 Dec 2007.
To cite this article: Will Tyson , Reginald Lee , Kathryn M. Borman & Mary AnnHanson (2007) Science, Technology, Engineering, and Mathematics (STEM) Pathways:High School Science and Math Coursework and Postsecondary Degree Attainment,Journal of Education for Students Placed at Risk (JESPAR), 12:3, 243-270, DOI:10.1080/10824660701601266
To link to this article: http://dx.doi.org/10.1080/10824660701601266
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RESEARCH PAPERS
Science, Technology, Engineering,and Mathematics (STEM) Pathways:
High School Scienceand Math Coursework
and Postsecondary Degree Attainment
Will TysonDepartment of Sociology, University of South Florida
Reginald LeeKathryn M. Borman
Department of Anthropology, University of South Florida
Mary Ann HansonCenter for Career and Community Research\
This article examines how high school science and mathematics course-taking cre-ates pathways toward future baccalaureate degree attainment in science, technology,engineering, and mathematics (STEM) majors in Florida 4-year universities usingBurkam and Lee’s (2003) course-taking categories developed using national studentdatasets. This study finds that even though women, overall, complete high-levelcourses, they do not complete the highest level science and mathematics courses.Even women who did complete high-level science and mathematics are less likelythan men to obtain STEM degrees. Black and Hispanic students complete lower levelhigh school courses, but Black and Hispanic students who did take high-level coursesare as likely as White students to pursue STEM degrees. Findings suggest that genderdisparities in STEM occur because women are less likely to pursue STEM, but racialdisparities occur because fewer Black and Hispanic students are prepared for STEMin high school.
JOURNAL OF EDUCATION FOR STUDENTS PLACED AT RISK, 12(3), 243–270Copyright © 2007, Lawrence Erlbaum Associates, Inc.
Requests for reprints should be sent to Will Tyson, University of South Florida, Department of So-ciology, 4202 E. Fowler Ave, CPR 107, Tampa, FL 33620. E-mail: [email protected]
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This project applies the science and mathematics course-taking categories devel-oped by Burkam and Lee (2003) to a cohort of Florida high school graduates to ex-amine racial–ethnic, social class, and gender disparities in high school course-taking, baccalaureate degree attainment, and degree attainment in science, tech-nology, engineering, and mathematics (STEM). Burkam and Lee’s research is im-portant because the categories they developed allow researchers to consider dis-parities between and among groups that constitute the focus of this article.Emerging research finds that women, Black and Hispanic students, and studentswith lower socioeconomic status (SES) typically do not pursue STEM degrees atthe same rate as men, White and Asian students, and students with higher SES.STEM attainment is determined by both educational attainment and pursuit ofSTEM coursework leading to STEM degrees. This study begins to distinguish be-tween educational disparities and STEM disparities by examining how race, class,gender, and high school course-taking predict STEM degree attainment amongbaccalaureate degree recipients.
The accumulating evidence concerning the importance of science and mathe-matics coursework in high school is overwhelming. High-level coursework inthese areas is important for student learning and leads to significant outcomes in-cluding college attendance and graduation (Schneider, Swanson, & Riegle-Crumb,1998). Rapid advances in technology and the movement toward a global economyhave increased the importance of knowledge in general, and in science and mathe-matics specifically (Friedman, 2005). Global economic changes have led to dra-matic increases in numbers of workers needed in STEM careers (National ScienceBoard [NSB], 2006). These changes increase the need for more high school stu-dents who complete rigorous science and mathematics coursework that preparesthem for STEM careers.
Science and mathematics course-taking is a key component on the pathwaytoward STEM careers. This STEM pathway can be understood as the set of edu-cational and occupational pathways that lead to STEM careers. These pathwaysinclude high school course-taking, transition to higher education, completion ofa baccalaureate degree in a STEM major, school to work transition, andworkforce participation. This study focuses on the link between science andmathematics high school course-taking and baccalaureate degree attainment byexamining how science and mathematics courses form high school STEM path-ways and race, class, and gender disparities in taking these courses. Althoughacknowledging its importance, we do not consider the role of job-related experi-ence in this article. It is important to understand course-taking patterns amongwomen, ethnic minorities, and low SES students and how these factors influenceparticipation on STEM pathways to explain race, class, and gender disparities inSTEM careers.
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BACKGROUND
High School Course-Taking and Educational Outcomes
Over the past decade, research on high school course-taking patterns has consis-tently shown that enrollment in advanced-level science and mathematics courses isrelated to a variety of important student outcomes. Rigorous high school course-taking is related to college aspirations (Burkam & Lee, 2003) and persistence(Horn & Kojaku, 2001). Such high-level course-taking predicts future college at-tendance and degree attainment (Schneider et al., 1998). Schneider and colleagues(1998) found that the effects of course-taking are much more important than the ef-fects of background factors such as parental education or income. Students whotake more challenging course sequences greatly increase their chances of enrollingin postsecondary institutions (Schneider, 2003).
Research finds that the challenges of rigorous course-taking are related to highschool success and proficiency in science and mathematics. Many studies havefound strong relationships between advanced science and mathematics course-work and scores on tests designed to measure science and mathematics achieve-ment (Adelman, 2006; Csikszentmihalyi & Schneider, 2000; Lee & Frank, 1990).For example, Riegle-Crumb (2006) found that more rigorous course-taking, par-ticularly in science and mathematics, strongly influences Scholastic Aptitude Test(SAT) performance.1 Similarly, an American College Testing (ACT) report dem-onstrated that course sequences (and the highest course completed) relate directlyto ACT mathematics scores, which in turn are related to the probability of successin students’ first college mathematics courses (Wimberly & Noeth, 2005). Further,Madigan (1997) demonstrated that students’ earlier achievement levels could notaccount for the effects of rigorous course-taking on achievement. Madigan (1997)showed that students who took more rigorous science courses had greater in-creases in science proficiency, regardless of their initial proficiency levels, and thatthe rigor of science courses was more important than the number of sciencecourses for increasing proficiency. Students who took physics had the largest in-creases, and students who took chemistry had larger increases than those who tookneither physics nor chemistry. Burkam and Lee (2003) found that course-takingwas strongly related to 12th-grade mathematics achievement and proficiency2 andalso had a substantial effect on gains in proficiency from 8th to 12th grade.
HIGH SCHOOL STEM PATHWAYS AND DEGREE ATTAINMENT 245
1It is worth noting that although there is some debate as to the validity of SAT scores for predictingcollege outcomes, SAT performance remains a deciding factor in determining who is admitted to manycolleges and universities.
2Mathematics achievement and proficiency were measured using the National Educational Longi-tudinal Survey (NELS) mathematics test.
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Research on Science and Mathematics Course-Taking
The studies previously discussed found that the content and rigor of courses takenwas more important than the number of courses or credits in determining a numberof positive educational outcomes. This contrasts with early research that indicatedthat the number of science and mathematics courses taken in high school was mostimportant in determining a number of subsequent outcomes (National Committeeon Excellence in Education, 1983). Contemporary research examines the rigor,content, or sequence of courses taken; however, these constructs can be understoodin different ways.
Schneider et al. (1998) identified sequences of science and mathematicscourses with respect to low, intermediate, and high levels of rigor for courses takenin the 10th, 11th, and 12th grades. Other research has focused on the highest levelcourse completed successfully, and these course sequences are often referred to asthe pipeline (Burkam & Lee, 2003). These highest level courses can be groupedinto relatively broad categories or into more fine-grained categories, and Burkamand Lee (2003) demonstrated that more detailed or fine-grained categorizations (intheir research, 8 instead of 5 categories or levels)3 are markedly superior to less de-tailed breakdowns in predicting future behavior and achievement in STEM-relatedcoursework. It is also worth noting that relatively small proportions of studentsgenerally complete the highest level courses or course sequences. For example,Schneider and colleagues (1998) found only 12% of high school seniors success-fully completed the highest mathematics course sequence, and only 22% com-pleted the highest science course sequence.
Gender and Race Differences in Mathand Science Coursework
In recent years, differences between males and females and between and amongracial and ethnic groups in the number of science and math courses taken havevirtually disappeared (Davenport et al., 1998). The research shift from numberto rigor has found that women are still less likely to take physics than men, butare actually more likely to take several other types of high-level science classes(e.g., Advanced Placement Biology or Advanced Placement Chemistry). Womenare only somewhat less likely than men to take high-level mathematics courses
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3Using the High School Effectiveness Supplement (HSES) of NELS:88 student transcript data forstudent course credits for courses taken in mathematics during high school and using the highest levelmathematics course completed by a student with a passing grade during high school, the following cate-gories were derived: No Mathematics; Nonacademic; Low Academic; Middle Academic I; Middle Ac-ademic II; Advanced Academic I; Advanced Academic II (Pre-Calculus); Advanced Academic III(Calculus).
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and are more likely to take standard courses such as college preparatory up toAlgebra II (Davenport et al., 1998). STEM-related differences between youngmen and women during high school are small and often insignificant and scienceand mathematics course-taking patterns and achievement are very similar (Na-tional Science Foundation [NSF], 1999), yet women are less likely to begin andcomplete STEM-related baccalaureate degrees (NSF, 1996). The origins of gen-der disparities in STEM degree attainment are unclear, given the convergence ofhigh school STEM pathways among men and women (NSB, 2006). This sug-gests that gender disparities on STEM pathways may emerge in college and thetransition from higher education or college to work, but this may not be the caseamong women with the highest levels of high school science and mathematicscourse-taking.
The STEM-related gap among ethnic minority groups has not disappeared.During high school, Blacks and Hispanics are less likely than Whites and Asians totake high-level mathematics and science courses, are more likely to take remedialmathematics, and score substantially lower on measures of both mathematics andscience achievement (NSF, 1999). Racial disparities in STEM degree attainmentmay be due to disparities in STEM course-taking, but general educational dispari-ties means there is a smaller pool of Black and Hispanic students available to pur-sue STEM degrees in college.
Increasing Importance of College Degrees
College attendance and graduation have consistently been found to relate to futureearnings (Sewell, Hauser, & Wolf, 1980). More and more jobs require a college de-gree and increasing percentages of workers have either acquired some post-secondary education or obtained a degree (Levy, 1998; Levy & Murnane, 1992).Looking to the future, there is every reason to believe that the demand for col-lege-educated workers will continue to grow along with the income divide be-tween those who have some postsecondary education and those who do not. In1973, only 28% of workers aged 25–49 had any postsecondary education. Today,59% of these workers have attended some type of postsecondary institution. As theshare of workers with postsecondary education has increased, the wage advantagesof college-educated workers have also gained ground. Employment shifts experi-enced during the latter half of the 20th century are expected to continue and in-crease throughout the current decade. Jobs that require an associate degree are ex-pected to grow the fastest, increasing by 32% through 2010, followed by jobs thatrequire a baccalaureate degree, growing by 24% (Hecker, 2001). Schneider (2003)argued that if these trends hold, young people who hold only a high school diplomawill unlikely to be able to support a family and maintain a reasonable lifestyle.
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These trends partly explain why more students aspire to and attend college thanever before (Schneider & Stevenson, 1999).
Increasing Importance of Science and MathematicsKnowledge and Careers
Other recent changes and trends in the U.S. economy point toward an increase inthe importance of high school science and mathematics course-taking. Employ-ment in science and engineering (S&E) occupations during the current decade willincrease much faster than employment in all remaining occupations (NSB, 2006).S&E occupations are projected to grow by 26% from 2002 to 2012, and employ-ment in all occupations is projected to grow 15% over the same period (Bureau ofLabor Statistics [BLS], 2004). This may be an underestimate of the need for sci-ence and mathematics education, because approximately 12.9 million workers saythey need at least a baccalaureate degree level of knowledge in S&E fields, butonly 4.9 million were in occupations formally defined as S&E. The urgent need forSTEM workers presents enormous challenges to our nation’s future productivityand to its educational systems.
Lack of Diversity in STEM Careers
American high schools and universities do not produce sufficient numbers of stu-dents who pursue and persist in STEM careers. Long-term trends indicate a steadyincrease in the number of foreign graduate students in the science and engineeringfields in the United States (NSB, 2002), but post-9/11 restrictions immigration andwork visas limit the availability of foreign students. Increasing diversity is an ef-fective way to replenish the STEM workforce. Traditionally, White and Asian menhave filled STEM occupations, but many women and Black and Hispanic studentsforgo pathways toward STEM careers. Blacks, Hispanics, and American Indianstogether made up 23% of the U.S. population in the mid-1990s, but only 6% of thetotal science and engineering labor force (Blacks 3%, Hispanics 3%, American In-dians less than 1%). Women made up 51% of the U.S. population and 46% of theU.S. labor force, but only 22% of scientists and engineers in the labor force (NSF,1999). Even among recent graduates (those who graduated in 1990 or later),women represented only 30% of the science and engineering labor force.
Much is at stake in the process of creating and maintaining the flow of studentsthrough the STEM pipeline; society must achieve a thoroughgoing understandingof how individual student occupational career lines in STEM are sustained duringthe course of student careers in secondary and postsecondary settings. Studentswho take more rigorous science and mathematics courses in high school are onpathways toward better educational outcomes and STEM careers. Based on thesetrends, one can expect that science and mathematics course-taking in high school
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will continue to increase in importance both to individuals and to the larger society.Identifying those courses that are critical in defining STEM outcomes, in particu-lar, the achievement of a baccalaureate degree in a STEM field and understandingcourse-taking trends across a cohort of high school graduates constitute an impor-tant step in understanding STEM pathways.
Opportunities Available in Florida
Florida is a unique study case, in part because of its growing population diversityand the wide-ranging employment opportunities in the STEM fields from theSpace Coast (Brevard and Volusia counties) focus on exploration to the marine sci-ence focus along the southwest coastal counties (Dade, Monroe, and Pinellas). AsFlorida has moved from agriculture to technoculture, the need for a more educatedworkforce has become paramount (Borman & Dorn, 2007). Currently, companiesrelocating to Florida often encourage transfer so as to bring their institutionalknowledge south. More important, Florida had the foresight to build administra-tive datasets to respond to policy questions.
Florida is the nation’s fourth largest state with a demographic profile that re-sembles the nation as a whole. In addition, Florida is home to several metropolitanuniversities: 11 universities comprise the State University System (SUS) and 28institutions form the Community College System, which has taken on an increas-ingly larger share of higher education activity, a trend expected to continue.Florida also has a well-articulated public secondary and postsecondary system.High school course offerings, although not precisely uniform across all 67 districtsin the state, are similar enough to invite cross-district comparisons and to positlinkages with course work offered in postsecondary settings. Postsecondary courseofferings are aligned across universities in the Florida SUS, and also acrossFlorida’s 2-year community colleges.
Florida’s thriving technology community provides employment in a wide vari-ety of STEM careers. The Interstate Highway–4 High-Technology Corridor Initia-tive, a cooperative program between universities and businesses, has promoted thegrowth of high-technology industry across central Florida. Florida is home to theSpace Coast and many scientists, mathematicians, and engineers involved inNASA programs and technology companies working with the military. Florida’saccountability system employs an extensive testing program and has a system ofrewards and punishments in place for low-performing schools. Finally, Florida hasa strong research base, with several world-class public and private researchuniversities.
In addition, the Florida Department of Education maintains student-tracking da-tabases that provide an extraordinary resource for this research. The Florida Educa-tion and Training Placement Information Program (FETPIP) database tracks stu-dent outcomes after high school for all Florida high school graduates. FETPIP
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collectsdataquarterlyandhasbeen trackingallhighschoolgraduatingcohorts since1991. FETPIP records contain information about students’ placement and employ-ment, military enlistments, and other important outcomes. Florida also has in place astate-of-the-art student data warehouse, which links the student high school and col-lege transcripts maintained by the Florida Department of Education (FLDOE).These, in turn, are linked to the outcomes collected by FETPIP, allowing researchersto examine how patterns of course-taking (Hallinan & Kubitschek, 1999) relate toimportant educational and career pathways taken by these graduates.
Research Questions
An emerging line of research examines STEM pathways conceived as course se-quences. This research is relevant to policy on opportunity structures and access be-cause it attempts to link curricular flows, academic tracks, and course-taking pat-terns to student educational outcomes (McFarland, 2006; Riegle-Crumb, Farkas, &Muller, 2006). Student-level analyses focus on the process by which students pro-ceed toward careers in STEM fields. Macro-level analyses of STEM pathways ex-amine race, class, and gender disparities in STEM outcomes and progression towardcompletion of STEM baccalaureate degrees. STEM degree disparities are the resultof disparities at all levels of education, but particularly through high school and col-lege. This examination of race, class, and gender disparities is important to under-standing how race, class, and gender structure student STEM pathways.
This study attempts to understand attrition from STEM pathways by examiningrace, class, and gender disparities in high school science and mathematicscourse-taking and race, class, gender disparities in STEM baccalaureate degree at-tainment among those students who took high-level courses in high school. Analy-ses examine STEM attrition or persistence early in the STEM pathway at highschool and link these early outcomes to STEM degree attainment, a crucial transi-tion point on the pathway toward STEM careers.
This study has three research objectives. First, this study adapts earlier researchon high school science and mathematics course-taking categories (Burkam & Lee,2003) to classify courses taken by a state-wide cohort of Florida public high schoolgraduates. Second, this article describes race, class, and gender disparities in highschool science and mathematics course-taking using these categories. Third, thisarticle determines how high school course-taking, race, class, and gender predictSTEM baccalaureate degree attainment among all baccalaureate degree recipients.These objectives lead to three key questions.
1. What levels of high school science and mathematics course-taking are re-lated to future STEM baccalaureate degree attainment among all degree re-cipients?
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2. How do students of different race, class, and gender groups differ in sci-ence and mathematics course-taking levels?
3. How does high school course-taking account for these disparities in STEMattainment?
This article integrates findings related to these research questions to make rec-ommendations for further research, including ways to update course-taking cate-gories to further integrate achievement to better identify students likely to obtainbaccalaureate degrees in STEM fields.
METHODOLOGY
This article employs descriptive statistics and logistic regression analyses to deter-mine the relationship between race, class, and gender and high school science andmathematics course-taking and achievement. This article describes course-takingpatterns among a diverse cohort of Florida public high school graduates. Logisticregression analyses assist in determining the extent to which race, class, and gen-der influence science and mathematics course-taking and STEM outcomes.
Data
The data for this study is from a subset of the Florida Longitudinal Education andEmployment Dataset obtained from the FLDOE. This study examines studentsfrom the population of 94,078 students who graduated from a Florida public highschool in 1996–1997. These students graduated from over 350 schools throughoutthe state of Florida. This longitudinal dataset describes high school course-takingand achievement, post-high-school enrollment and employment, and postbacca-laureate enrollment and employment within the state of Florida through the2003–2004 school year.
These data were collected by the Florida Department of Education K–20 Edu-cation Data Warehouse (EDW) starting in 1995. The EDW collects informationfrom Florida public institutions, vocational technical schools, community col-leges, and public 4-year universities. EDW data also do not include student enroll-ment in Florida private colleges and universities or enrollment in any institution
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4These data also include courses taken at over 30 local community colleges and universities. Only4,421 (4.7%) students took science and mathematics courses at a community college or university. It isunclear how many of these courses were taken for high school credit and how many were taken while astudent was dual enrolled in high school and college. FLDOE does not report course-taking for 2,930graduates. It is unclear if these students did not take any science or mathematics courses or if overallcourses were not reported.
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outside the state of Florida. This dataset includes over 426,000 science and mathe-matics courses taken and grades received by 91,148 students primarily during the1995–1996 and 1996–1997 school years, when students were in the11th and 12thgrades.4
Additional analyses were conducted on the 16,587 1996–1997 Florida publichigh school graduates who obtained a baccalaureate degree from a Florida SUSuniversity within 6 years of high school graduation.5 This subset makes up 17.6%of all high school graduates. These students include 2,324 students who graduatedwith a STEM baccalaureate degree. These students are 14.0% of all baccalaureatedegree recipients and only 2.5% of all Florida high school graduates.
Demographic Variables
The primary independent variables for this study are race, social class, and gender.Gender is coded as male or female using biological sex. Race is limited to five cate-gories: White, Black, Hispanic, Asian, and Other. Other includes Native Ameri-cans, multicultural students, and students for whom no race is given. Class is deter-mined using free lunch as a proxy. Students enrolled in free lunch programs werecoded 1 for free lunch. Students not enrolled in free lunch were coded 0.
Degree Attainment Variables
Baccalaureate degree attainment and high school course-taking are the dependantvariables in this study. Degree attainment is classified by the 2-digit Classificationof Instructional Programs code that describes the 39 broad areas of baccalaureatedegrees earned by 1996–1997 high school graduates. This includes six STEM ma-jors: Engineering, Agricultural Sciences, Chemistry/Physics, Biology, Mathemat-ics, and Computer Science. Students who graduated with any of these majors arecoded 1 for STEM degree. Students who did not graduate with any of these majorsare coded 0 for STEM degree.
Mathematics Course-Taking Variables
Burkam and Lee (2003) developed science and mathematics course-taking catego-ries to move beyond measures of course credits or number of courses completed asthe standard in this line of research. The resulting measures attempt to create a
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5The Florida SUS is made up of 11 institutions. The University of Florida, Florida State University,Florida Agricultural & Mechanical University, University of South Florida, Florida Atlantic University,University of West Florida, University of Central Florida, Florida International University, and Univer-sity of North Florida granted degrees during the 1996–1997 school year. Florida Gulf Coast Universityand New College of Florida began granting degrees in 1997–1998 and 2001–2002, respectively.
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pipeline to better represent student course-taking histories. Burkam and Lee(2003) created mathematics course-taking categories by taking advantage of thenatural sequence of mathematics courses and dividing mathematics courses intononacademic courses and academic courses. Nonacademic courses include gen-eral math, basic math, and consumer math courses. Academic courses are dividedamong three categories. Low academic courses include preliminary courses suchas Prealgebra or courses low in rigor such as Algebra 1A and Algebra 1B, and Al-gebra I split over two academic years. Middle academic courses begin with Alge-bra I and include Algebra II. Advanced academic courses include challenging,highly rigorous courses such as Trigonometry and Probability and Statistics, aswell as Precalculus, Calculus, and courses such as Multivariate Calculus and Dis-crete Mathematics. Burkam and Lee (2003) used previous research to separate themiddle and advanced academic courses into two and three divisions, respectively,to create eight mathematics categories based on the highest course taken.
This study adapts this strategy to code the highest mathematics course taken inthe 11th or 12th grade by Florida public high school graduates. Burkam and Lee(2003) only gave credit to a student who achieved a passing grade. Mathematicscourse-taking levels in this study are the same as those used by Burkam and Lee(2003) as previously described, including the highest course in which a studentachieved a C or above.6 The eight mathematics categories in this study are as listed:
0. No mathematics—Student did not complete any mathematics courses witha C or above
1. Nonacademic—Nonacademic courses (general, basic, consumer)2. Low academic—Prealgebra, Algebra IA, or Algebra 1B3. Middle academic I—Algebra I, Geometry4. Middle academic II—Algebra II5. Advanced I—Trigonometry, Analytical Geometry, Probability/Statistics6. Advanced II—Precalculus7. Advanced III—Calculus, AP Calculus AB, AP Calculus BC
The most frequently occurring mathematics category in Burkam and Lee’s(2003) national data set is middle academic I at 22.7%, followed by middle aca-demic II at 20.7%. An additional 21.5% of students were in the lowest three cate-gories, and the remaining 34.9% are in the highest three groups including 11.1%who took Calculus and above.
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6The dataset includes letter grades without plus/minus distinctions (A, B, C, D, F). Additional des-ignations include unsatisfactory and incomplete. Courses with a P for passing were included. Concur-rent research suggests receiving a D in a course does not yield a significant advantage over not takingthe course at all.
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Science Course-Taking Variables
Science course-taking categories are more difficult to develop because there is noset sequence for science courses. Schools differ in curricula and prerequisites, butat many schools it is possible for students to take a wide variety of courses. Theprimary difference between the national dataset used by Burkam and Lee (2003)and the Florida high school data used in this study is that the national data set in-cludes a limited set of courses, particularly in Chemistry and Physics. Biology Iwas the most frequently taken course at 68% in the national data set used byBurkam and Lee (2003). Chemistry was second at 41%, followed by Physical Sci-ence (39%), Earth Science (18%), and Physics (18%). Using the average grade atwhich each science course was taken and sequences within each category, Burkamand Lee (2003) constructed a Life Science pipeline, Chemistry pipeline, Physicspipeline, and a pipeline that included all other physical sciences (e.g., Earth Sci-ence, Physical Science, etc.) forming a seven category pipeline:
0. None—Student did not complete any science courses with a C or above1. Primary Physical Sciences—Physical Science, Earth Science2. Secondary Physical Sciences—Astronomy, General Physics, Introductory
Chemistry3. Secondary Life Sciences—Ecology, Honors and General Biology 2, Ad-
vanced Biology4. Chemistry 1 or Physics 15. Chemistry 1 and Physics 16. Chemistry 2 or Physics 2
BurkamandLee(2003) foundthatmoststudents fail tomovepast theprimaryandsecondary levels (54.9%). The Primary Physical Sciences category has the most stu-dents at 35.4%. A quarter of students take Chemistry 1 or Physics 1, and only 12.2%takeboth.Only7.1%ofstudents takeChemistry2orPhysics2.Buildingacrosswalkbetween Burkam and Lee’s (2003) categories and Florida high school sciencecourses is more difficult than with mathematics courses because Burkam and Lee(2003) constructed course-taking categories from a narrow set of courses and sci-ence courses do not have a defined sequence. In addition, Florida has a broad sciencecurriculum appealing to students with a range of academic abilities, allowing stu-dents to take a wide range of courses in the 11th and 12th grades.
Because this study is limited to 11th- and 12th-grade mathematics and sciencecourses taken by the students in this cohort, students who take Physics are placedinto Category 5. Many students take Chemistry during 9th or 10th grade; thus, stu-dents who take Physics only in the 11th and 12th grade most likely have takenChemistry before. Category 4 becomes Chemistry 1 only and Category 5 is PhysicsI. This category includes students who took Physics 1 if they did not complete
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Chemistry 2. Florida also offers remedial science courses that do not classify asPrimary Physical Sciences. Category 0 is Low-level Sciences and includes studentswho did not complete any science course with a grade of C or above.
RESULTS
Table 1 shows the percentage of all students in each science and mathematics cate-gory group. Both science and mathematics course categories show a somewhatnormal distribution. Students are primarily concentrated in the middle categories.The None science category includes students who did not take any science in the11th and 12th grades, as well as students who took remedial science courses. Thelow math category is similar in that it includes students who did not complete anymathematics or remedial math with a grade of C or more.
For over a quarter of students, the highest science course completed is a Sec-ondary Life Sciences course such as General Biology, Ecology, Zoology, or Ma-rine Biology. An additional 22.3% of students peak at the Chemistry I-only leveland 24.7% students at the Any Physics level. Only 425 students took Chemistry IIor Physics II. These categories represent the role of Biology, Chemistry, and Phys-ics as core sciences in this science pipeline. Over 72% of graduates completed aBiology, Chemistry, or Physics course with a C or above.
Almost 40% of students fall into what Burkam and Lee (2003) called the mid-dle academic level of mathematics. Over 16% of students stopped at middle aca-demic I in Algebra I or Geometry and an additional 22.7% stopped at middle aca-demic II in Algebra II. A total of 25,860 (28.4%) took mathematics courses beyondAlgebra II at the advanced academic level, including 12.4% of students who tookcourses such as Trigonometry, Analytic Geometry, and Probability and Statistics
HIGH SCHOOL STEM PATHWAYS AND DEGREE ATTAINMENT 255
TABLE 1Highest Science and Mathematics Course Category Completed
by All Students
Science N % Mathematics N %
Low-level sciences 11,027 12.1 No mathematics 11,966 13.1Primary physical sciences 6,371 7.0 Non-academic 17,046 18.7Secondary physical sciences 7,564 8.3 Low academic 884 1.0Secondary life sciences 22,938 25.2 Middle academic I 14,736 16.2Chemistry I only 20,344 22.3 Middle academic II 20,656 22.7Physics I 22,479 24.7 Advanced I 11,336 12.4Chemistry II or Physics II 425 0.5 Advanced II 7,512 8.2
Advanced III 7,012 7.7Total 91,148 100.0 Total 91,148 100.0
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at advanced I. This group also includes 8.2% of students who took Precalculus butdid not complete a Calculus course in the advanced I level. An additional 7.7% ofstudents took Calculus courses or higher level mathematics courses (i.e., DiscreteMath, Multivariate Calculus) in the advanced III level.
Similarities in the distribution of science and mathematics courses within thesecategories should be noted. Both science and mathematics have a somewhat nor-mal distribution centered in the middle categories (Biology/Chemistry and Geom-etry/Algebra II). Both categories have the same percentages of students at the highlevels of the distribution. Just over a quarter of students are in Physics and aboveand 28.4% of students are in the highest mathematics categories (Trigonome-try/Statistics, Precalculus, and Calculus).
Science and Mathematics Course-takingand STEM Degree Attainment
One purpose of this analysis is to use the Burkam and Lee (2003) classificationsystem to predict the actual STEM degree attainment of students in the 1996–1997Florida high school cohort among the 16,587 students who completed a baccalau-reate degree from a Florida public 4-year university within 6 years. These analysesaddress Question 1 to understand how levels of high school science and mathemat-ics course-taking are related to future STEM baccalaureate degree attainment.
Tables 2 and 3 show the number of students in each science and mathematicscourse-taking category who went on to obtain a baccalaureate degree (BA) from a
256 TYSON ET AL.
TABLE 2Likelihood of Baccalaureate Degree (BA) and Science, Technology,
Engineering, and Mathmatics (STEM) Attainmentby Science Course Category
BA/All STEM/BA STEM Among BA recipients
Mathematics Total N % N % B SE Exp (B)
No mathematics 11,966 765 6.4 46 6.0 .161 .169 1.175No academic 17,046 451 2.6 15 3.3 –.459 .273 .632Low academic 884 34 3.8 1 2.9 –.586 1.018 .557Middle academic I 14,736 822 5.6 40 4.9 –.062 .178 .940Middle academic II 20,656 3,835 18.6 198 5.2 — — —Advanced I 11,336 4,315 38.1 440 10.2 .735 .089 2.086 ***Advanced II 7,512 3,093 41.2 453 14.6 1.148 .089 3.152 ***Advanced III 7,012 3,272 46.7 1,131 34.6 2.272 .082 9.703 ***Intercept –2.911 ***Pseudo R2 .149Total 91,148 16,587 18.2 2,324 14.0
***p < .001. **p < .01. *p < .05
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Florida public 4-year university and the percentage of those baccalaureate recipi-ents who went on to obtain STEM degree. These tables also support the data re-ported by showing the logistic regression of course-taking category by STEM at-tainment among all baccalaureate degree recipients using the middle category as acomparison group.
Table 2 shows that students who complete advanced mathematics courses havehigher educational attainment within Florida than their classmates at Algebra II(middleacademicII)orbelow.Around40%ofstudents in theTrigonometryandSta-tistics (advanced I) andPrecalculus (advanced II)groups inhighschoolobtainabac-calaureate degree from a Florida 4-year university within 6 years. Calculus makesthe largest contribution to degree attainment with 46.7% of students who take thiscourse successfully in high school obtaining a Florida public university degree.
Among degree recipients, students in Calculus courses are more likely to obtainSTEM degrees (34.6%). The primary difference between science and mathematicsis that even though Trigonometry and Statistics (10.2%) and Precalculus (14.6%)course levels produce more STEM students than courses categorized in lower lev-els, these courses still produce less than the rate for all baccalaureate students(14.0%). Students who take advanced academic level courses are more likely toobtain a baccalaureate degree from a Florida public 4-year university. Among all
HIGH SCHOOL STEM PATHWAYS AND DEGREE ATTAINMENT 257
TABLE 3Likelihood of Baccalaureate Degree (BA) and Science,
Technolgy, Engineering and Mathmatics (STEM) Attainmentby Science Course Category
BA/All STEM/BA STEM among BA recipients
Science Total N % N % B s.e. Exp(B)
None 11,027 548 5.0 35 6.4 –.348 .183 .706Primary Physical
Sciences6,371 250 3.9 18 7.2 –.220 .251 .803
Secondary PhysicalSciences
7,564 343 4.5 18 5.2 –.557 .249 .573 *
Secondary LifeSciences
22,938 2,232 9.7 137 6.1 –.391 .104 .676 ***
Chemistry I only 20,344 3,982 19.6 351 8.8 — — —Physics I 22,479 9,051 40.3 1,693 18.7 .867 .062 2.380 ***Chemistry II or
Physics II425 181 42.6 72 39.8 1.922 .162 6.833 ***
Intercept –2.336 .056Pseudo R2 .057Total 91,148 16,587 18.2 2,324 14.0
***p < .001. **p < .01. *p < .05
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baccalaureate degree recipients, students in these categories are more likely to geta STEM degree. This table shows the large influence of Calculus. Almost half ofthe 2,324 STEM degree recipients in the 1996–1997 high school graduating class(1,131, 48.7%) took a Calculus course. The rest of the table confirms the effects ofhigher level course-taking on STEM degree attainment.
The logistic regression in Table 2 shows students in the advanced academiclevel are significantly more likely than students in the middle academic II group toobtain a STEM major. This shows the large difference between taking Algebra IIand advanced courses. Students in this Algebra II category are not more likely thanstudents in Algebra I or Geometry to obtain a STEM degree. These students doseem much more likely to obtain a baccalaureate degree. Only 5.6% of middle aca-demic I students eventually complete a baccalaureate degree in Florida comparedto 18.6% of middle academic II students. Table 3 provides similar evidence forBurkam and Lee’s (2003) conceptualization of science courses.
Table 3 shows that students in the Physics I and Chemistry II or Physics II cate-gories obtained a baccalaureate degree from a Florida university more often thanstudents in the Chemistry I only or lower categories. Over 40% of students in thesescience groups in high school obtain a baccalaureate degree from a Florida 4-yearuniversity within 6 years. Among these students, students in who completedChemistry II or Physics II are more likely to obtain STEM degrees. Students in thePhysics I category obtain STEM degrees at 18.7%, above the 14.0% the rate for allbaccalaureate degree recipients. The Chemistry II or Physics II group produces thelargest percentage of STEM students at 39.8%, but this is only 72 of 425 students.Physics course-taking is a primary factor in STEM attainment. Almost 3/4 of bac-calaureate degree recipients (1,693, 72.8%) took Physics I. This suggest some ad-vantage over students who took Chemistry, even though 1,417 (61.0%) of studentswho took Chemistry completed a STEM bachelor’s degree. Still, only 8.8% of stu-dents who took Chemistry I, but not Physics I completed a STEM bachelor’s de-gree. Logistic regression of STEM degree attainment among all baccalaureate de-gree recipients provides more evidence on the influence of course-level on STEMdegree attainment.
Logistic regression analyses shows that students in the highest levels (Physics Iand Chemistry II or Physics II) are significantly more likely than students in theChemistry I only group to obtain a baccalaureate degree in a STEM major. Thisfinding provides more evidence that students who took Physics but not Chemistryin the 11th and 12th grade probably took Chemistry in the 9th or 10th grade. Thisfinding may also suggest that Physics I, Physics I with Honors, AP Physics B, orAP Physics C are higher level courses than comparable Chemistry I courses, eventhough science does not follow the same sequence as mathematics.
Table 3 regression also shows that students who stop at Life Sciences are signif-icantly less likely than students in the Chemistry I category to complete a STEMdegree. This provides more support for Burkam and Lee’s (2003) method of plac-
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ing Biology courses at a lower level than Chemistry and Physics. Biologycourse-taking in the 11th or 12th grade does not provide a strong route towardSTEM degree completion among baccalaureate degree recipients, at least withouttaking Physics courses.
These data are limited in determining the effects of course-taking and achieve-ment on university enrollment or baccalaureate degree attainment, because thisdataset does not include postsecondary outcomes for students who attend universi-ties outside of Florida or private in-state universities. These data are well-suited foranalyses that determine factors influencing students to obtain STEM degrees andexamining how race, class, and gender disparities in course-taking and achieve-ment influence disparities in STEM degree attainment.
Demographic Disparities in STEM outcomes
This analysis establishes a relationship between high school course-taking andSTEM baccalaureate degree attainment in response to Question 1. The secondquestion addresses race, class, and gender disparities in high school science andmathematics course-taking because race, class, and gender disparities exist inSTEM degree attainment. Table 4 shows the race, class, and gender disparities inpostsecondary outcomes.
Women make up just over half of all 1996–1997 graduates and 21.5% of themgo on to graduate from a Florida public 4-year university, compared to 14.6% of
HIGH SCHOOL STEM PATHWAYS AND DEGREE ATTAINMENT 259
TABLE 4Race, Class, Gender of Future Bachelor’s (BA) and Science, Technology,
Engineering, and Mathmatics (STEM) Students
BA/All STEM/BA
Variable Total % N % N %
GenderWoman 48,135 52.8 10,355 21.5 996 9.6Man 43,013 47.2 6,232 14.5 1,328 21.3
RaceWhite 42,640 46.8 11,546 27.1 1,481 12.8Black 16,791 18.4 1,910 11.4 234 12.3Hispanic 12,036 13.2 1,963 16.3 291 14.8Asian 1,891 2.1 841 44.5 275 32.7Other 1,203 1.3 327 27.2 43 13.1
ClassNot free lunch 68,994 75.7 14,810 21.5 2,037 13.8Free lunch 20,741 22.8 1,768 8.5 286 16.2Unknown 1,413 1.6 9 0.6 1 11.1
Total 91,148 100.0 16,587 18.2 2,324 14.0
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men. But only 9.6% of those female baccalaureate degree recipients graduate witha STEM baccalaureate degree, compared to 21.3% of men. This table also showsthe racial disparity in postsecondary outcomes. Asian and White students are mostlikely to obtain a baccalaureate degree at a Florida public four-year university, butalthough 32.7% of Asian students obtain STEM degrees, only 12.8% of White stu-dents obtain STEM degrees. Hispanic (16.3%) and Black (11.4%) students obtainfewer baccalaureate degree degrees at Florida public 4-year universities, but theHispanic and Black students who obtain baccalaureate degrees obtain STEM de-grees at a similar rate as White students. This suggests that racial disparities inSTEM pathways occur in high school when students get on the STEM pathwaywith science and mathematics course-taking, but these racial disparities may nothave a strong influence in college among talented Black and Hispanic students.Fewer Black and Hispanic students complete college, but those who do are not be-hind White students in STEM attainment.
Using free lunch as a proxy, class seems to show similar findings as race. Classdisparities exist in obtaining a baccalaureate degree, but free lunch students whoobtain baccalaureate degrees obtain STEM degrees at a higher rate than studentswho were not on free lunch programs in high school. This suggests that class dis-parities influence STEM pathways in high school, but not in college among stu-dents who persist towards a baccalaureate degree.
Among students who obtain a baccalaureate degree from a Florida 4-year pub-lic university within 6 years of graduation, women are far less likely to complete aSTEM major than men, but race and class disparities are limited to Asian students’advantage over students of other races. To better understand how high school andcollege provide STEM pathways to STEM degree attainment, it is necessary to ex-amine race, class, and gender disparities in high school course-taking.
Demographic Disparities in High School Course-Taking
The second research question asks if students in different race, class, and gendergroups differ in their high school science and mathematics course-taking patterns.To address demographic disparities in attrition from STEM pathways in highschool and college, it is necessary to examine how race, class, and gender are re-lated to differences in science and mathematics course-taking and achievement.Tables 5 and 6 describe demographic disparities among all 1996–1997 students.The logistic regression analyses in Tables 7 and 8 examine the likelihood of stu-dents taking higher level science and mathematics courses.
Table 5 shows that women generally complete higher level courses slightlymore often than men, and there are few differences on course-taking at the highestlevels. Around 25% of both women and men take Physics and more men takeChemistry II or Physics II, even though it is a small number. Mathematics showssimilar trends, as 22.8% of women take Advanced I or Advanced II courses com-
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TABLE 5Gender and Class Disparities in High School Science and Mathematics Course-Taking
Girl Boy Not Free Lunch Free Lunch Missing
Course N % N % N % N % N %
ScienceNone 5,050 10.5 5,977 13.9 7,372 10.7 3,165 15.3 490 34.7Primary Physical Sciences 2,919 6.1 3,452 8.0 3,993 5.8 2,153 10.4 225 15.9Secondary Physical Sciences 3,563 7.4 4,001 9.3 5,208 7.5 2,186 10.5 170 12.0Secondary Life Sciences 12,700 26.4 10,238 23.8 16,572 24.0 5,972 28.8 394 27.9Chemisty I only 11,600 24.1 8,744 20.3 15,949 23.1 4,300 20.7 95 6.7Physics 12,137 25.2 10,342 24.0 19,514 28.3 2,926 14.1 39 2.8Chemisty II or Physics II 166 0.3 259 0.6 386 0.6 39 0.2 0 0.0
MathematicsNo mathematics 5,569 11.6 6,370 14.8 8,499 12.3 3,063 14.8 404 28.6Nonacademic 8,410 17.5 8,636 20.1 11,476 16.6 5,053 24.4 517 36.6Low academic 452 .0
9432 1.0 602 0.9 249 1.2 33 2.3
Middle academic I 7,536 15.7 7,200 16.7 9,918 14.4 4,545 21.9 273 19.3Middle academic II 11,617 24.1 9,039 21.0 15,999 23.2 4,536 21.9 121 8.6Advanced I 6,646 13.8 4,360 10.9 9,795 14.2 1,512 7.3 29 2.1Advanced II 4,319 9.0 3,193 7.4 6,464 9.4 1,019 4.9 29 2.1Advanced III 3,559 7.4 3,453 8.0 6,241 9.0 764 3.7 7 0.5
Total 48,135 52.8 43,013 47.2 68,994 75.7 20,741 22.8 1,413 1.6
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262
TABLE 6Race Disparities in High School Science and Mathematics Course-Taking
White Black Hispanic Asian Other
Course N % N % N % N % N %
ScienceNone 5,154 9.5 3,054 16.3 2,487 17.8 170 6.2 162 10.6Primary Physical Sciences 3,496 6.5 1,759 9.4 937 6.7 75 2.7 104 6.8Secondary Physical Sciences 4,385 8.1 1,848 9.9 1,128 8.1 108 4.0 95 6.2Secondary Life Sciences 13,461 24.8 5,058 27.0 3,624 25.9 458 16.8 337 22.0Chemistry I Only 12,126 22.4 4,268 22.8 3,043 21.7 551 20.2 356 23.3Physics 15,236 28.1 2,690 14.4 2,756 19.7 1,331 48.7 466 30.5Chemistry II or Physics II 328 0.6 24 0.1 24 0.2 39 1.4 10 0.7
MathematicsNo mathematics 6,070 11.2 3,017 16.1 2,479 17.7 215 7.9 185 12.1Nonacademic 10,067 18.6 4,248 22.7 2,262 16.2 231 8.5 238 15.6Low academic 419 0.8 244 1.3 192 1.4 11 0.4 18 1.2Middle academic I 7,167 13.2 4,249 22.7 2,882 20.6 236 8.6 202 13.2Middle academic II 12,381 22.8 4,156 22.2 3,250 23.2 503 18.4 366 23.9Advanced I 8,028 14.8 1,401 7.5 1,211 8.7 468 17.1 228 14.9Advanced II 5,212 9.6 850 4.5 917 6.6 382 14.0 151 9.9Advanced III 4,842 8.9 536 2.9 806 5.8 686 25.1 142 9.3
Total 54,186 59.4 18,701 20.5 13,999 15.4 2,732 3.0 1,530 1.7
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263
TABLE 7Logistic Regression of Science and Mathematics Course-Taking By Race, Class, and Gender
Model 1a Model 2b Model 3c Model 4d
Demographic B SE Exp(B) B SE Exp(B) B SE Exp(B) B SE Exp(B)
Gender.206Women
—
.014 1.229 *** .068 .016 1.070 *** .221 .015 1.247 *** –.069 .025 .933 **
Men — — — — — — — — — — —
Race
—White–.356
— — — — — — — — — — —
Black–.233
.019 .701 *** –.632 .024 .531 *** –.832 .024 .435 *** –.968 .048 .380 ***
Hispanic.888
.020 .792 *** –.316 .024 .729 *** –.471 .023 .624 *** –.307 .040 .736 ***
Asian.187
.043 2.430 *** .987 .040 2.683 *** 1.021 .040 2.775 *** 1.287 .047 3.623 ***
Other .053 1.206 .175 .057 1.191 ** .087 .055 1.090 .098 .090 1.103
Class–.569Free lunch
–2.263.018 .566 *** –.685 .023 .504 *** –.675 .022 .509 *** –.693 .041 .500 ***
Missing—
.091 .104 *** –2.545 .163 .078 *** –2.165 .128 .115 *** –2.829 .380 .059 ***
Not free lunch.019
— — — — — — — — — — —
Intercept.058
.011 –.865 .013 *** –.728 .012 *** 2.215 .020 ***
R2 *** .066 *** .085 *** .058 ***
**p < .01. ***p < .001.aDependant variable is Chemistry I only courses and above. bDependant variable is Physics I courses and above. cDependant variable is Advanced I courses
and above. dDependant variable is Advanced III courses and above.
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264
TABLE 8Logistic Regression of Science, Technology, Engineering, and Mathmatics Baccalaureate Degree Attainment By Race, Class,
and Gender Among High Science Course-Taking Students
Model 1a Model 2b Model 3c
Demographic B SE Exp(B) B SE Exp(B) B SE. Exp(B)
GenderWomen –.926 .046 .396 *** –.887 .055 .412 *** –.895 .077 .409 ***Men — — — — — — — — —
RaceWhite — — — — — — — — —Black .041 .080 1.042 .167 .099 1.181 .228 .159 1.256Hispanic .191 .072 1.211 ** .294 .087 1.342 *** .254 .125 1.289 *Asian 1.166 .081 3.208 *** .938 .093 2.555 *** .932 .122 2.539 ***Other .038 .168 1.039 –.098 .224 .907 –.192 .287 .826
ClassFree lunch .103 .075 1.108 .138 .093 1.148 .072 .134 1.074Missing –.413 1.068 .662 .361 1.227 1.135 — — —Not free lunch — — — — — — — — —
Intercept –1.435 .035 *** –1.111 .041 *** –.339 .057 ***R2 .066 *** .065 *** .083 ***Total 16,587 100.0 9,232 55.7 3,272 19.7
Notes. *p < .05. **p < .01. ***p < .001.aSubsample includes all 1996-1997 baccalaureate degree recipients. bSubsample includes all students at Physics I courses and above. cSubsample includes
all students at Advanced III courses and above.
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pared to 19.1% of men. Men are more likely to take Advanced III Calculus at 8.0%compared to 7.4%. T tests confirm that women are significantly more likely tocomplete higher level courses, but not the highest level courses. These findingssuggest that high school course-taking may be a minimal influence on STEM attri-tion among female baccalaureate degree recipients, but race and class may influ-ence gender disparities.
Table 5 also shows that students in free lunch programs are far less likely tocomplete higher level science and mathematics course than students who are not infree lunch programs. Only 14.1% of free lunch students take courses beyondChemistry I only and 15.9% take Advanced academic mathematics courses beyondAlgebra II, compared to 28.8% and 32.6% of students who are not free lunch stu-dents, respectively.
Table 6 shows broad disparities between racial groups in course-taking. Asianstudents far outpace their peers. A majority of Asian students (50.1%) take sciencecourses beyond Chemistry I and 56.2% take mathematics courses beyond AlgebraII. This includes 30.1% in Chemistry I and Physics I, 1.4% in Chemistry II or Phys-ics II, and 25.1% in Calculus. White students are behind Asian students but wellahead of Black and Hispanic students. Over 28% of White students take coursesabove Chemistry I, including 15.2% who take Chemistry I and Physics I. Over athird of White students peak at advanced mathematics courses, including 8.9% inCalculus courses.
Black and Hispanic students have the poorest course-taking patterns. A major-ity of students in both groups do not take Chemistry I. A small group of Black stu-dents take Physics (14.4%) and only 24 students take Chemistry II or Physics II.Only 14.9% of Black students take advanced academic level mathematics courseincluding 2.9% who take Calculus. Hispanic students show similar patterns, withonly around 20% taking Physics and above and 15.3% taking Trigonometry/Statis-tics or Precalculus. Only 24 students take Chemistry II or Physics II and 5.8% takeCalculus. These findings provide evidence that high school is a primary point forBlack and Hispanic students to drop off STEM pathways because they do not takehigh-level courses at the same rate as their peers.
Science and Mathematics High-Level Course Progression
The data reported in the previous tables show that there are race, class, and genderdisparities in the highest level science and mathematics courses taken. Tables 7 and8 test the significance of the influence of these demographic variables on highschool course-taking. Model 1 of Table 7 examines demographic effects on reach-ing the Chemistry I only science level and above. The Chemistry I level is distinctof the Life Sciences level even though previous analyses show that students whotake Physics are more likely to graduate with a STEM degree. Model 2 examinesrace, class, gender effects on Physics and Chemistry II or Physics II course-taking.
HIGH SCHOOL STEM PATHWAYS AND DEGREE ATTAINMENT 265
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Model 3 examines demographic effects on reaching the Advanced I (Trigi-nometry/Statistics) mathematics level and above. Students in the Advanced I levelare significantly more likely to graduate with a STEM degree even though studentsat Advanced I and Advanced II are less likely than students in Advanced III tocomplete a STEM degree. Model 4 examines demographic effects on reaching theAdvanced III (Calculus) mathematics level.
Table 7 confirms that there are demographic differences at the highest levels ofscience course-taking. Model 1 shows that among all graduates, women are signif-icantly more likely than men to take courses above Life Sciences. But Model 2continues to show that women are significantly more likely than men to take Phys-ics and above, as well. This further suggests that disparities in STEM degree attain-ment may not be due to high school preparation. There may also be more distinctdifferences between courses that explain the influence of high school factors oncollege attainment. Gender shows different effects in predicting mathematicscourse-taking levels. Model 3 shows that women are more likely to take courses inTrigonometry/Statistics or above, but Model 4 shows that women are significantlyless likely to take Calculus when controlling for race and class.
Race and class show consistent effects in all four models. Black and Hispanicstudents are significantly less likely than White students to take higher level sci-ence and mathematics courses. Students in free lunch programs are significantlyless likely than students who are not in free lunch programs to take high levelcourses. The low Pseudo R2 in all three models indicates that several factors be-yond race, class, and gender influence course-taking and successful completion ofthese courses at a grade of C or above. Additional student characteristics such asmore comprehensive proxies for socioeconomic status, as well as school charac-teristics such as curricula may be important factors. These findings also suggestthat the course-taking categories may not be as comprehensive as necessary totruly explore racial disparities in high school course-taking.
Racial Disparities and Persistence
The remaining question addresses the STEM baccalaureate degree attainment offuture baccalaureate degree recipients who were in the highest course-taking andachievement categories in high school. Analyses of STEM baccalaureate degreeattainment among the high-level students who do complete degrees help explainhow race, class, and gender influence STEM outcomes among those students whopersist in their educational pathways. Table 8 examines the demographic influ-ences on STEM attainment among students at the highest course-taking levels.Model 1 of Table 8 examines the influence of race, class, and gender on STEM at-tainment among all baccalaureate recipients. Model 2 examines demographic ef-fects among baccalaureate recipients who completed Physics I or Chemistry II or
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Physics II course-levels. Model 3 of Table 8 examines degree recipients who wereat Advanced III Calculus mathematics course-taking and above in high school.
Table 8 shows that among students at the highest high school science course-taking categories, gender disparities in STEM degree attainment still exist, butrace and class disparities favor Hispanic students. Model 1 shows that among allbaccalaureate degree recipients, women are less likely to obtain a STEM degree.Models 2 and 3 show that this gender pattern is true for women at high science andhigh mathematics course-taking levels This suggests that university course-takingis a likely drop-off point off STEM pathways for women with high-levelcourse-taking in high school. Previous analyses show differences between menand women in high-level course-taking, but among students with the highestcourse-taking, gender disparity remains.
Hispanic and Asian students are significantly more likely than White studentsto obtain a STEM degree, and Black students are not significantly different fromWhite students at all course-taking levels. Even though throughout the STEMpathway Black and Hispanic students are significantly less represented than Whitestudents, Table 8 shows that Black students who were in high-level science coursesin high school and completed a baccalaureate degree are just as likely as White stu-dents with similar academic profiles to obtain a baccalaureate degree in a STEMfield. Hispanic students who completed high level science courses in high schoolare significantly more likely than White students who completed Physics and inhigh school to obtain a STEM baccalaureate degree. Asian students remain morelikely to obtain STEM baccalaureate degrees even when only accounting for stu-dents in the highest high school science categories. Student free lunch status inhigh school shows no significant effect. The low Pseudo R2 in Table 8 suggeststhat other factors influence STEM degree attainment. It is likely that these otherfactors discourage women at the highest high school course-taking levels frompursuing STEM degrees.
DISCUSSION AND CONCLUSION
This study was undertaken to understand pipeline progress using a unique Floridacohort data set. A secondary purpose was to sharpen measures of such progress byreplicating and extending the earlier seminal work of Burkam and Lee (2003). Thisprocess involved including both course-taking and course performance as mea-sured by the academic grade assigned to the student by the teacher. Although theseare important and useful issues, the larger equity concern explored in this articleconcerns the achievement of baccalaureate degrees by women and minorities, par-ticularly the achievement of STEM postsecondary degrees within 6 years follow-ing graduation from high school. It is of critical importance to determine how mi-
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norities and women fare during this process. Are they placed at risk or do theysucceed in achieving degrees in STEM?
Our findings reveal the importance and value of taking high-level courseworkin both mathematics and science during high school. Enrollment and attainment inphysics and calculus is particularly important for all students with respect to ob-taining a STEM degree down the road. Somewhat surprising and most encourag-ing, the results of our analyses show that minority students who are prepared forSTEM degree attainment by virtue of taking high-level science and mathematicscourses, particularly calculus, chemistry, and physics at the highest levels, aremore likely to persist through STEM coursework in college than their White coun-terparts and obtain a STEM degree. Those African American students with higherlevel coursework preparation who persevere in pursuing the pathway toward ob-taining a baccalaureate degree are just as likely to obtain STEM degrees as theirWhite peers who also complete baccalaureate degrees. Similarly, Hispanic stu-dents with advanced level course preparation are also more likely than White stu-dents to persist to obtain a STEM degree.
These findings run counter to a large number of studies in the research literatureon STEM attainment among minorities, but it is not a surprise when reflectingupon Table 4. Even though a smaller percentage of Black (11.4%) and Hispanic(16.3%) students obtain baccalaureate degrees in Florida compared to White stu-dents (27.1%), the differences among these students with respect to numbers ofSTEM baccalaureate degrees awarded is negligible. The specific factors discour-aging minority students from completing their baccalaureate degrees and degreesin STEM fields likely accounts for attrition along educational pathways. In ourfindings, STEM pathways are well traveled by persistent students, many of whomare minority students.
Although the findings reported here bode well for minority students who persistin college even when their preparation in STEM is relatively weak, our findings re-veal some discouraging findings in the case of the women in this 1997 Florida highschool cohort. For example, we know that women in this study are as likely as mento enroll in challenging coursework (with the exception of coursework in physics).However, they do not persist in achieving STEM degrees in the same numbers astheir male counterparts of all racial and ethnic backgrounds. In their analyses ofsynthetic cohorts of women in science and engineering, Xie and Shauman (2003)concluded that although there have been large gains by women in these fields withrespect to both increases among science and engineering degree recipients and inthe science and engineering labor force, these gains are not substantial. They as-serted that gender differences in individual career aspirations may be at the heart ofthe matter (Xie & Shauman, 2003). This assertion leads us to the question whetherwomen leave the STEM pathway in high school or college. They noted that overthe life course, men and women may react differently to career setbacks, withwomen more likely than men to forgo their career goals altogether and to replacethem with family responsibilities. In other words, women may be less
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well-prepared for STEM majors during high school and find that the long hoursand demands during the early career years may overwhelm the desire to pursue aSTEM career.
An important aspect of these findings for policymakers at the school district andschool levels is that it is critically important that schools find ways to offer oppor-tunities for all students to enroll in the highest level courses in mathematics andscience, for if they do, students taking these courses are more likely to persist in theSTEM pathway regardless of race or ethnicity. Different strategies may be in orderto support persistence by women. In other analyses not reported here, we have de-termined that schools that may be resource-constrained and unable to offercoursework at the highest levels may encourage students to enroll at neighboringcommunity colleges to complete such courses. Further research will examine thestructural effects of schools and communities on the opportunities students have totake high-level science and mathematics courses. Once students are provided ac-cess to such coursework in 11th and 12th grades, our analyses clearly show thatthey are well on the pathway toward STEM degree attainment and subsequent ca-reers in related fields.
ACKNOWLEDGMENTS
The research reported here was supported by a grant from the National ScienceFoundation (NSF 0337543) awarded to the third and fourth authors. We gratefullyacknowledge the support of NSF in assisting with the work that was carried out inthis study. We also gratefully acknowledge the assistance of Jay Pfeiffer and SamArcangeli of the Florida Department of Education in kindly providing access to thedata used in the analyses reported here.
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